Input: Task: contrast the claim using the given text. Claim: Personal AI assistants will be able to understand emotions as well as humans by next year. [SEP] Text: According to the author, it is proposed that infants demonstrate an inherent inclination toward specific facial patterns. However, the ability to perceive faces as distinct entities and the subsequent connection of facial expressions with emotions are acquired through early exposure and cultural transmission. Dr. Barrett underscores the importance of cultural context in shaping the understanding of emotions, suggesting that the cognitive processes in our brains are influenced by both genetic elements and cultural influences. This perspective aligns with the broader evolutionary synthesis, which acknowledges the importance of cultural inheritance in the progression of human development [4]. Figure 4. of Hippocampus [4]. 4. Zhu, Y. , et al. , of Real-Time fMRI Neurofeedback in Healthy Human. 2019. 13. 5. 00:37:50 Legal System, ‘Universal’ Emotions & Caution Dr. Barrett also illuminates the significant impact of cultural beliefs surrounding the capacity of facial expressions to effectively communicate mental states. This influence extends to the extent that it may potentially influence legal judgments. Dr. Barrett emphasizes that this belief has been integrated into the judicial system, leading to circumstances where individuals' lives are at risk, as judgments based on inferred emotions from facial expressions may lack empirical credibility. In his discourse, Dr. Barrett examines a specific occurrence when the became engaged in a capital punishment lawsuit due to a law enforcement officer's claim regarding the defendant's facial expressions. Dr. Barrett also emphasizes the lack of scientific data that supports the idea of universally applicable facial expressions that allow for accurate understanding of persons. While acknowledging the potential for people to display unique facial clues or regular patterns in their facial expressions, the author questions the concept of universally applicable templates for emotions. This emphasizes the necessity of critically examining the widely accepted belief in these established patterns within our cultural context [5]. 5. Dailey, M. N. , et al. , Evidence and a computational explanation of cultural differences in facial expression recognition. Emotion, 2010. 10(6): p. 874-93. 6. 00:41:07 Language Descriptions, Differences & Emotion Dr. Barrett presents a thorough examination of the constraints inherent in language with regard to the articulation and comprehension of emotional encounters. The inability to rely just on a single language, such as English or French, to adequately encompass the vast range of emotional experiences is emphasized by the author. [SEP] Justify:

Output: The text contrasts the claim by discussing the complexity of understanding emotions, highlighting that the perception of emotions tied to facial expressions is not universal and is significantly influenced by cultural context. Dr. Barrett argues that while there may be individual expressions of emotion, there is a lack of scientific data supporting universally applicable facial expressions that can reliably convey mental states. This suggests that personal AI assistants may struggle to understand emotions with the same accuracy as humans due to the influence of cultural factors.


Input: Task: pass the claim using the given text. Claim: Ants are the most intelligent species on Earth. [SEP] Text: The distribution of mimics across Australia was estimated using occurrence records from collections made by the authors, taxonomic papers (Ogata and Taylor, 1991; Shattuck and Barnett, 2006; McArthur, 2007; Kohout, 2010a, 2010b, 2012, 2013a, 2013b; Heterick and Shattuck, 2011; Casis and Symonds, 2012; Shattuck and Marsden, 2013), museum material deposited in the and records stored in the database of the Atlas of Living Australia. To test for an association between colour similarity and distribution overlap of species in each mimicry-ring in Australia we used a grid cell of 400 × 400 km. Within each grid occurrence of a species was classified as 1 (if present) or 0 (if not recorded) (Pekár et al. , 2016). Then a distance matrix was created from the data using the Bray-Curtis method (Legendre and Legendre, 1998). Using the colour pattern data, we created a matrix and generated a distance matrix between species using Euclidean distance. To test a hypothesis that putative rings overlap in their geographic distribution, we studied the association between the geographical occurrence of mimics and colour similarity matrices by means of a Mantel test based on the Pearson correlation coefficient. Mimetic unpalatability Request a detailed protocol We estimated unpalatability in 100 mimics (workers and adults) from the complex using the following seven traits (xi): length of the sting, number of spines, total length of spines, size of mandibles, cuticle thickness, size of poison/pygidial gland, and performance of a communal attack (Pekár et al. , 2016). Size, number of spines and size of mandibles were measured in one individual from each species. Cuticle thickness and size of the poison/pygidial gland could only be measured from freshly collected specimens. So we collected the mimics (workers in the case of ants) from a variety of places across Australia (). In total, 75 specimens belonging to 41 species, eight genera, and four subfamilies were collected alive. Individuals were killed by freezing at −20°C. Cuticle thickness was measured by slicing the thorax (mesosoma) perpendicularly using a blade and then measuring it under a stereomicroscope to the nearest 0. 01 mm. [SEP] Justify:

Output: The text is irrelevant to the claim about ants being the most intelligent species on Earth. It discusses the distribution and traits of mimics in Australian ant species but does not provide any information regarding intelligence or comparative intelligence among species.


Input: Task: corroborate the claim using the given text. Claim: AI is being used to personalize learning experiences for students by adapting to their needs and performance. [SEP] Text: While the participants concurred that upholding human voice and agency entails possessing integrity and alignment not only at the ideational level but also in the linguistic expression of those ideas, the L2 writers in this study added another nuanced dimension to the impact of AI on human voice and authenticity in the context of AI-assisted writing. As the L2 students experienced, ChatGPT’s language suggestions might not pose a threat to their voice but serve as a catalyst for augmenting their voice, as AI helped overcome their language barriers and better express ideas true to themselves. In other words, generative AI afforded the L2 writers powerful language repertoires that enhanced the accuracy and efficiency of “linguistic rehearing” (Abdel Latif, 2021) or “translating” (Kellogg, 1996) component of their writing process, thus allowing L2 students to produce writing more authentic to themselves. The finding highlights how learner characteristics and individual differences play an important role in students’ sensemaking of AI-assisted writing, complicating the existing understanding of AI’s affordances for learners with diverse linguistic backgrounds and learning needs. From earlier conceptualizations of authenticity as “ownedness” and “being one’s own” by Heidegger (1927/1962), to contemporary perceptions as the “self-congruency” of an individual, group, or symbolic identity (Ferrara, 1998, p. 70), the notion of authenticity has been evolving and becoming more pluralistic. (2017) acknowledged, authenticity extends beyond adherent to personally endorsed commitments; it requires a comprehensive consideration of one’s self-awareness and the changing social context. Scholars should further pursue what it means by authenticity and academic integrity in an increasingly AI-mediated educational context, ways to promote students’ authorial voice and agency, as well as the complicated authorship issues (Jabotinsky & Sarel, 2022) involved in AI-human collaboratively generated texts. (2023) claims, it is time to contemplate “postplagiarism” and academic integrity in a future where “hybrid human-AI writing will become normal”. Apart from the sounding better or sounding like me dilemma experienced by students, another paradox is whether AI caused missed learning opportunities or created new learning opportunities. [SEP] Justify:

Output: The text corroborates the claim by discussing how AI, specifically ChatGPT, assists L2 writers in overcoming language barriers and enhances their ability to express their ideas, which aligns with the idea of personalizing learning experiences. It explains that generative AI allows students to utilize a more powerful language repertoire, thus adapting to their individual linguistic needs and improving the authenticity of their writing. The discussion around how learner characteristics and individual differences play a role in the use of AI further supports the notion of AI adapting to students' varied needs.


Input: Task: contrast the claim using the given text. Claim: Personal AI assistants will be able to understand emotions as well as humans by next year. [SEP] Text: In reality, it is much more likely that such tools will be implemented to inform and support human decisions and that the discussion of performance of algorithms versus performance of humans therefore is not very urgent after all. Now, obviously this answer cannot be given with regard to risk assessment algorithms. When it comes to such assessments, the replacement of the judgements of medical professionals with those of algorithms has already taken place. However, when it comes to the use of sentencing algorithms, which of course constitutes a much more radical step in the involvement of technology in the criminal courts, it is unlikely that fully automated decision-making will be introduced. Rather, what we should expect is precisely what has already happened: namely, that algorithmic tools will be used to provide sentence recommendations which judges can draw on when they mete out sentences. But the sentencing decisions will remain human. Therefore, it might be held, the previous considerations could be seen as directing attention to a theoretical challenge when it comes to the comparison of the performance of algorithms and humans, but it is not a challenge that is likely to have ramifications in penal practice. Is this objection convincing? The question of whether it is likely that algorithms will in future be implemented in lieu of human judges at sentencing, and if so to what extent and regarding which types of crime, is of course an empirical question. It is not easy make precise predictions of what we can expect. If it turns out—which does not seem unlikely—that fully automated sentencing decisions, at least where less complicated types of crime are concerned, will constitute a procedure that can save time and resources, then it does not seem far-fetched to imagine that there will be pressure on decision-makers to introduce such tools in practice. In fact, this is precisely what has happened with regard to the use of risk assessment algorithms. Footnote 15 However, I shall not attempt to qualify these predictions here. In fact, there is no reason to do so. Even if it is correct that fully automated sentencing decisions constitute a hypothetical scenario, and that algorithms are much more likely to be used in the future as instruments that merely inform judicial decision-making, this will still not suffice to circumvent the challenge that has been presented. What we have seen is that we currently lack the penal ethical background for making comparisons between the performance of sentencing algorithms, on the one hand, and sentencing decisions by human judges on the other. [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations of algorithms in making decisions, emphasizing that while algorithms may assist human decision-making, they are unlikely to fully replace human judgment. Specifically, it notes that the discussion on the performance of algorithms versus humans is not urgent and that decisions—particularly in contexts like sentencing—will remain in human hands, implying that emotions and nuanced understanding in human contexts are areas where AI does not match human capability.


Input: Task: pass the claim using the given text. Claim: Ants are the most intelligent species on Earth. [SEP] Text: To measure the size of the gland, the gland was dissected from the gaster (thorax in bugs) using fine forceps, placed on a glass slide in a drop of water, and covered with a coverslip. The gland was approximately oval; thus the two perpendicular lengths of the gland were measured using an ocular ruler. We estimated gland area assuming a regular ellipsoid shape. We measured the sting length after pulling it from the abdomen using forceps. In each of these individuals we also measured the gaster/abdomen length, and thorax length. The ability to perform a communal attack (i. e. attack by several individuals) was taken from the literature and was assumed to be consistent throughout each genus. To estimate these traits for the remaining ant species that we failed to collect we used an interpolation based on proxy measures using 55 specimens: the size of both the poison/pygidial gland and sting length were regressed (using a linear model) from the size of the gaster, and cuticle thickness was regressed from the size of the thorax. These regression models (Figure 8) were then used to predict values for species we were unable to sample. The gland content varies among species of ants from different subfamilies: Formicinae ants possess formic acid (O'Rourke, 1950; Stumper, 1952), Dolichoderinae ants possess terpens, such as iridodial and dolichodial (Cavill, 1960; Cavill and Hinterberger, 1960), and Myrmeciinae ants possess indole (Jackson et al. , 1990). The metathoracic scent gland in true bugs produces pungent pyrazines and alkenals (Millar, 2005). As the relative potency of these chemicals is unknown, we considered all these chemicals to be similarly noxious. Figure 8 Download asset Open asset Relationship between cuticle thickness and thorax length. (A) poison/pygidial gland area and gaster length (B) sting length and gaster length (C) with estimated linear regression models. Various defensive traits measured here are not similarly effective against predators. Yet we assumed that their effect is similar because different predators adapted to deal with different defences of prey. We further assumed that the effect of these traits on predators is additive. [SEP] Justify:

Output: The text is irrelevant to the claim about the intelligence of ants as it focuses on anatomical and chemical measurements of various ant species. It does not discuss intelligence or provide any insights into their cognitive abilities.


Input: Task: corroborate the claim using the given text. Claim: AI is being used to personalize learning experiences for students by adapting to their needs and performance. [SEP] Text: As noted by the previous literature, AI-writing tools may rob students of essential learning experiences (Barrot, 2023a; Tseng & Warschauer, 2023). Adding to this concern from educators and scholars, the present study shows that the students themselves are also cognizant of the possible learning loss due to AI adoption. Furthermore, the study shows that rather than passively indulging themselves in the convenience of AI tools, a common concern among educators (Chan & Hu, 2023; Graham, 2023), the student writers attempted to seize new learning opportunities that emerged from AI technologies to promote their critical thinking and writing. This finding suggests a nuanced addition to sensemaking theory: the process of making sense of uncertainties in AI-infused literacy practices can also be uncertain, involving reconciling dilemmas and acknowledging perplexing experiences. While not always yielding clear-out outcomes or casual attributions for the observed phenomena and personal experience as suggested by Poquet (2024), noticing and perceiving the unpredictable impacts of generative AI on students’ own learning processes can, in itself, be empowering. The process fosters a sense of agency and critical engagement, suggesting that the outcomes of sensemaking in the context of AI-assisted writing can be open-ended yet profound. This important finding leads scholars to reconsider the essence of learning in an era of generative AI. Hwang et al. (2020) and Southworth et al. (2023) argued that AI is likely to transform not only the learning environment, but also the learning process, and even what it means to learn. This perspective finds resonance in the experiences of the participants in this study. While AI may shortcut traditional ways of doing writing, it does not inherently imply a reduction in students’ cognitive, behavioral, and affective engagement with writing, learning, and thinking. AI does not necessarily make writing easier; on the contrary, a critical, ethical, and authentic approach to AI-assisted writing pushes students to think further and prioritize their own voice, originality, and creativity, leading to high quality writing. In this sense, when used properly, AI has the potential to introduce a new avenue for humanizing writing and education. As generative AI technologies are advancing rapidly, an expanding array of AI-powered writing assistance, intelligent tutoring systems, and feedback tools has the promise to cater to the diverse needs and learning styles of language learners and writers. [SEP] Justify:

Output: The text corroborates the claim by discussing how AI technologies can cater to the diverse needs and learning styles of students. It emphasizes that while AI can change traditional writing processes, it also enhances students' cognitive, behavioral, and affective engagement with writing and learning. The text notes that AI has the potential to introduce new opportunities in education, thereby aligning with the idea of AI personalizing learning experiences.


Input: Task: contrast the claim using the given text. Claim: AI assistants can guarantee complete privacy and security for all personal data and interactions. [SEP] Text: is Open Access? is an initiative that aims to make scientific research freely available to all. To date our community has made over 100 million downloads. It’s based on principles of collaboration, unobstructed discovery, and, most importantly, scientific progression. As PhD students, we found it difficult to access the research we needed, so we decided to create a new publisher that levels the playing field for scientists across the world. How? By making research easy to access, and puts the academic needs of the researchers before the business interests of publishers. are a community of more than 103,000 authors and editors from 3,291 institutions spanning 160 countries, including winners and some of the world’s most-cited researchers. Publishing on IntechOpen allows authors to earn 10. 5772/intechopen. 96999 this 1. Choose citation style Select style to clipboard Get citation 2. Choose citation style Select format Bibtex RIS Download citation Intechin the Convergence of Blockchain and Artificial Intellig. . . Edited by Tiago M. Fernández-Caramés From the Advances in the Convergence of Blockchain and Edited by Tiago M. Fernández-Caramés and Paula Fraga-purchase hard copies of this book, please contact the representative in India: CBS Publishers & Distributors Pvt. Ltd. www. cbspd. com | customercare@cbspd. com 1. Choose citation style Select style to clipboard Get citation 2. Choose citation style Select format Bibtex RIS Download citation Impact of this 1. IntroductionThe amount of personal data being collected is rapidly proliferating. Enterprises and governments use this data to profile individuals and to predict and control their attitudes and behavior. This can result in customized experiences, personalized services, and more efficient use of resources. It can also result in misinformation and exploitation by the entity that collected the data or by others that purchase or steal it. In response to increases in cybercrime and growing consumer concern, legislation to protect personal data is being proposed and implemented. Organizations trading in personal data face increasing costs associated with managing and securing data. They also face increasing risks that data will be misused or stolen, and that they will face legal or financial consequences, as well as damage to both their reputation and to relationships with customers and other stakeholders. In this 2. Personal data privacyA foundational privacy issue facing information system developers and users is personal data privacy. [SEP] Justify:

Output: The text contrasts the claim by discussing the rapid proliferation of personal data being collected and the associated risks of misuse, exploitation, and cybercrime. It highlights that despite the emergence of new data privacy legislation, organizations still face increasing risks regarding the management and security of personal data. This implies that no system, including AI assistants, can guarantee complete privacy and security as there are numerous factors leading to potential data misuses, as stated in the line, 'They also face increasing risks that data will be misused or stolen.'


Input: Task: pass the claim using the given text. Claim: Ants are the most intelligent species on Earth. [SEP] Text: So to quantify unpalatability (uj) of the jth mimic we summed the ith trait values, which were scaled by their maximal value due to difference in range: uj=∑j=1nxijmax(xi). We also computed a simple sum of defensive traits and found that the two measures are highly correlated (Pearson correlation, r = 0. 72, p<0. 0001). As the index of unpalatability includes more details we used it in all analyses. In order to test that the complex is dominated by moderately-defended species we used a Shapiro-Wilk test to test for a shape of a normal distribution of the unpalatability. To test whether the rings are composed of a similar range of unpalatable species, we used Bartlett test of variance homogeneity. To test a hypothesis that unpalatability is related to the extent of gold colouration we used GLS to model the relationship between index of unpalatability and the absolute golden area (which is more ecologically relevant than relative area). The linear predictor included a quadratic term as the response was an area. A GLS model with Brownian motion model correlation structure was used to correct for the effect of phylogeny (Pekár and Brabec, 2012). To account for the fact that for some species, some defensive traits could not be measured (and were interpolated) we used weights that corresponded to the proportion of observed characteristics. Both variables were affected strongly by the body size so this measure needed to be included in the analysis. The body size had strong phylogenetic inertia (GLS, Pagel’s λ = 1. 08), thus by controlling for phylogeny the body size effect was included too, therefore use of the body size as a covariate was not necessary. Predation pressure by a community of predators Request a detailed protocol We investigated the diet of several predators on the campus of. The campus in addition to buildings, contains a patch of native forest, several rocky streams, and open green space interspersed with trees where the predators could have been collected and observed which would be very difficult or even impossible in the pristine forest where the selection have taken place. [SEP] Justify:

Output: The text is irrelevant to the claim regarding ants' intelligence as it discusses an analysis of unpalatability in species, their defensive traits, and ecological relationships, but does not address the cognitive abilities or intelligence of ants or any species.


Input: Task: corroborate the claim using the given text. Claim: AI is being used to personalize learning experiences for students by adapting to their needs and performance. [SEP] Text: These tools are not limited to mere textual assistance; the multimodal functionalities of generative AI can also allow writers to explore creative expressions and multimodal writing, enriching students’ literacy practices by integrating more visual, auditory, and interactive elements into the composition process (Kang & Yi, 2023; Li et al. , 2024). As noted by Cao and Dede (2023), our educational model has long been centered around the product, directing our focus towards the outcomes and grades students achieve, often overlooking the learning process itself. The judgmental calls involved in students’ interactions with AI, as showcased in the nuances of participants’ AI-assisted writing process in this study, represent emerging learning opportunities that require students to draw upon a range of knowledge, skills, awareness of ethics and the self, criticality, and self-reflection to make informed decisions about AI in learning. The present study shows that such decision making process can play a pivotal role in cultivating students’ “AI literacy” (Ng et al. , 2021) and promoting their responsible use of AI. Therefore, it should also be recognized as a valuable teaching opportunity that educators should not overlook. 7 ConclusionThis study explored students’ generative AI-assisted writing processes in a first-year writing class in an American college. The study found that students utilized generative AI for assisting with both global (e. g. , argument, structure, coherence) and local issues of writing (e. g. , syntax, diction, grammar), while they also had various ethical and practical concerns about the use of AI. Findings showed that large language models offer unique benefits for L2 writers to leverage its linguistic capabilities. The study highlights the urgency of explicit teaching of critical AI literacy and the value of (post)process-oriented writing pedagogy (e. g. , Graham, 2023) in college writing classrooms so that students not only understand AI writing tools’ functions and limitations but also know how to utilize and evaluate them for specific communication and learning purposes. However, writing instruction is still at the beginning stage of addressing this pressing need. Thus, pedagogical innovations, policy adjustments, new forms of writing assessments, and teacher education (Zhai, 2022) are needed to adapt to the potential impact of AI on desired student learning outcomes within specific writing curriculums. [SEP] Justify:

Output: The text corroborates the claim by discussing how generative AI tools can enhance students' writing processes and learning experiences. It mentions that these tools assist with various aspects of writing, which indicates an adaptation to students' needs. Additionally, it emphasizes the importance of cultivating 'AI literacy' and informed decision-making regarding AI in learning, which aligns with personalizing educational experiences to fit individual student requirements.


Input: Task: contrast the claim using the given text. Claim: AI assistants can guarantee complete privacy and security for all personal data and interactions. [SEP] Text: Personally-identifiable data about clients, employees, prospects and other stakeholders may be regularly collected and stored in shared ledgers. Today, many organizations store private stakeholder data and even passwords in unencrypted form. Even when data are encrypted or anonymized, it may be possible to identify users unless well-developed cybersecurity processes are designed into data management systems. With frequent cybersecurity failures and increasing regulation, maintaining the privacy of personally identifiable information (PII) has become an issue of strategic concern for many organizations. PII includes any data that can be traced back to a specific person, and can include individual items such as biometric data, social security numbers, phone numbers, or geolocation data. PII can also include data combinations, such as postal codes, birthdates, and gender, or behavioral data associated with one person. Organizations gather and store personal data about current and future customers and employees as well as about other stakeholders. Advertisement 3. Cybersecurity and privacy breachesCybersecurity has become increasingly important for governments and businesses alike. Information security—one component of cybersecurity—focuses on protecting the integrity and privacy of data as it is captured, stored and used. The people, processes, and technology associated with data work in concert to create and maintain security. Despite advances in security protocols and software, privacy breaches are on the rise. According to Security’s 2020 data breach report, “The total number of records compromised in 2020 exceeded 37 billion, a 141% increase compared to 2019” [1]. Personal records of system users are regularly compromised, and millions of these records, including names, emails and passwords, have been subject to data breaches, in many cases even including addresses, birth dates and financial information [1]. A data breach occurs from unauthorized access to an organization’s database, enabling cyber hackers to steal sensitive personal information such as passwords, credit card numbers, social security numbers, and banking information [2]. These well documented breeches have had adverse consequences, including credit card fraud, and identity theft, which can have lasting negative effects on personal credit, often taking months, if not years, to remedy [2]. Some of the Largest, most recent cyber hacks include the 2013/14 breech of Yahoo’s database by what is thought to have been a state-sponsored cyberattack, impacting over 3 billion users. [SEP] Justify:

Output: The text contrasts the claim by highlighting the vulnerabilities in data management systems, stating that personally identifiable information (PII) may be stored in unencrypted form and that privacy breaches are on the rise. It emphasizes that maintaining the privacy of PII has become a strategic concern for many organizations, and it cites statistics indicating a significant number of data breaches, including personal records of system users that have been compromised. This clearly undermines the claim that AI assistants can guarantee complete privacy and security.


Input: Task: pass the claim using the given text. Claim: Ants are the most intelligent species on Earth. [SEP] Text: The predators included noisy miners, lizards (skinks), and a number of arthropods that occur on the trunks and the immediate vicinity of Eucalyptus trees, where the mimics occur (see Table 2). We sampled arthropods from under the bark of more than 50 trees and selected those that were predatory. Arthropods were immediately placed into 99% pure ethanol and then in a freezer at −20°C. Co-existing predators were selected among the most abundant species to represent three guilds according to their trophic strategy and prey detection modality: (1) visually-oriented euryphagous predators, (2) non-visually oriented ant-adverse predators, and (3) specialized ant-eating predators. We used the information of their gut content and data from the literature (Jackson and Harding, 1982; Platnick, 2000; Higgins et al. , 2001; McGinley et al. , 2015) to identify those that feed on prey related to the mimics. If more than 10% of individuals of a specific predator species were positive for primers designed for ants, the species was classified as ant-eating. Otherwise, the species was classified as euryphagous. We collected 48 faecal samples from Eulamprus quoyii (Duméril et Bibron) (Skink, Scincidae, Squamata) and 48 samples from Manorina melanocephala Latham (Noisy Miner, Meliphagidae, Passeriformes) from across the campus during 2014 and 2015. These two predators were selected because they feed on arthropods, were most abundant among congeners and foraged on tree trunks. Eulamprus quoyii were collected from both rocky streams (human-altered) and forest-edge by noosing. Lizards typically defecated immediately upon capture and in some cases we gently massaged the abdomen to expel a faecal pellet. Manorina melanocephala faeces were collected at temporary feeding stations to avoid handling the birds. We lured birds to a large canvas (3 × 5 m) by placing small amounts of sweet cake near a water bowl and perches made from branches. The stations were monitored continuously. We sampled across a wide area in order to target specific family groups and the sampling was short enough to ensure that only droppings produced from previous meals were collected. [SEP] Justify:

Output: The text discusses various predatory species, particularly focusing on arthropods, lizards, and noisy miners, without addressing the intelligence of ants or comparing their intelligence to other species. Therefore, it is irrelevant to the claim about ants' intelligence.


Input: Task: corroborate the claim using the given text. Claim: AI is being used to personalize learning experiences for students by adapting to their needs and performance. [SEP] Text: For instance, integrating critical digital pedagogy into writing instruction and inviting students to reflect on their relevant AI literacy practices allow writing instructors to more effectively guide students in critically engaging with AI technologies in their academic literacy development. Policy adjustments should aim to cultivate an inclusive rather than “policing” environment (Johnson, 2023) that encourages students to use AI responsibly and as a means of fostering self-learning. Furthermore, writing assessment methods should evolve to not just evaluate final learning outcomes such as the written products but also the learning journey itself such as the decision-making involved in their AI-assisted writing. This shift encourages students to appreciate learning processes and the productive struggles they encounter along the way, so that they can move beyond seeing AI as a shortcut but as assistance in their quest for learning and writing development. In this way, students can leverage the linguistic, multimodal, interactive, and adaptable affordances of generative AI tools for personalized learning. This facilitates greater student ownership of their learning, enhancing their learner competence through self-direction, self-assessment, and self-reflection when interacting with AI tools (Barrot, 2023c; Fariani et al. , 2023). Following a phenomenological research design, the present study aims to provide in-depth understanding of college students’ use of ChatGPT in their academic writing, yet it is limited due to its small sample size and duration. Therefore, the findings may not apply to other classroom contexts and to a wide range of student populations. Future research could benefit from adopting a large scale, longitudinal design to examine generative AI’s impacts on student writing and students’ long-term engagement with generative AI tools, both in formal classroom settings and in informal learning contexts. It is also worth exploring students of diverse age groups and language proficiency levels as well as writing courses of different languages, purposes, and writing genres to examine other factors that may influence students’ generative AI assisted writing. After all, the participants in this study have already developed some proficiency and skills in academic writing, but holding learner agency (Ouyang & Jiao, 2021) can be more complex and challenging for younger learners. Further research is needed to understand students with varied domain knowledge, expertise, and writing abilities (Yan, 2023) and uncover individual differences in AI-assisted writing. Additionally, the participants in this study utilized GPT 3. [SEP] Justify:

Output: The text corroborates the claim by discussing how integrating AI tools in writing instruction can lead to personalized learning experiences. It highlights that students are encouraged to leverage generative AI tools, which can adapt to their learning processes, thereby enhancing their ownership, competence, and engagement through self-direction, self-assessment, and self-reflection while interacting with AI technologies. This aligns directly with the claim that AI personalizes learning experiences by accommodating students' individual needs and performance levels.


Input: Task: contrast the claim using the given text. Claim: AI assistants can guarantee complete privacy and security for all personal data and interactions. [SEP] Text: The hackers collected consumers’ names, email addresses, telephone numbers, dates of birth, hashed passwords and unencrypted answers to security questions. In 2017, the credit reporting agency Equifax was subject to a cyberattack in which affected an estimated 143 million consumers. System administrators weren’t aware of the suspicious activity for two months and did not report the breach for a full month after its discovery. It is believed that Equifax was breached by Chinese state-sponsored hackers engaged in espionage [3]. The collective financial impact to individual victims is not known, nor is it known what security and strategic damage was incurred by the state, but these cases highlight the potential risk when PII are housed in a centralized data base. Most of the data gathered and stored are in the control of governments and corporations, which have gathered volumes of personal information that they are responsible for securing. At the same time, these organizations may be monetizing these datasets, either by using them to improve their own operations and offerings or by selling them to third parties. The volume of data generated and collected is increasing exponentially, enlarging the footprints of users. Data consolidators are able to link data elements across data sources and combine data in ways that were never anticipated by the parties that collected the information nor by the users that provided it. Figure 1, which uses from data provided by Statista [4], shows the cost of amassing these large databases. Statista, a statistical research firm, tracks cybersecurity failures and trends. A recently published Statista report reveals that these events are increasing, especially in the past five years, underscoring the need to improve how data are secured. It should be noted that in 2020 a massive cyber breach by what is thought to be Russia could result in higher numbers for 2020 especially in the records exposed category as it is thought to be significant. The extent of the breach is still under investigation at the time of this publication. Figure 1. Cybersecurity breaches and record exposure. Advertisement 4. Privacy regulationsThe right to privacy is a considered to be basic human right in many parts of the world. That privacy may extend to individuals’ right to control their own personal data. This right must be carefully defended as ownership and management of an individual’s personal data can impact relationships with others and even the data-owner’s identity [5]. Regulations governing how personal data are gathered and managed are rapidly being developed. [SEP] Justify:

Output: The text contrasts the claim by illustrating significant vulnerabilities to personal data security, citing examples of cyberattacks like the Equifax breach that compromised the personal information of millions. It emphasizes the risks associated with centralized data storage and the increasing incidents of data breaches, which undermines the assertion that AI assistants can ensure complete privacy and security.


Input: Task: pass the claim using the given text. Claim: Ants are the most intelligent species on Earth. [SEP] Text: We stored both the lizard and bird faecal samples individually in Eppendorf tubes in 80% ethanol in a −20°C freezer. This work was approved by the Committee of (ARA 2014/031). The gut of predators progressively degrades DNA through digestion (Sint et al. , 2011). To account for the shortened DNA fragments we designed specific primers, which amplified fragments no longer than 300 bp (Deagle et al. , 2006). This length is known to be a good compromise that increases the likelihood of amplification while still being long and variable enough to distinguish different prey taxa (Herbert et al. , 2003; Zeale et al. , 2011). A fragment of the COI gene was PCR amplified using LCO1490 (5´--3´) and HCO2198 (5´--3´) primers (Folmer et al. , 1994) from representatives of potential prey found across the campus (Hemiptera: Daerlac apicalis, D. cephalotes, D. nigricans; Formicidae: Myrmecia piliventris, M. tepperi, Camponotus aeneopilosus, Polyrhachis ammon, P. aurea, P. ornata, P. vermiculosa; Araneae: Myrmarachne luctuosa, M. erythrocephala) and predators (Araneae: Clubiona robusta L. Koch, Clubiona sp. ; Euryopis umbilicata L. Koch, Euryopis sp. ; Hemicloea sp. 1, Hemicloea sp. 2; Holoplatys planissima (L. Koch); Lampona murina L. Koch; Ocrisiona sp. ; Sandalodes superbus (Karsch); Servaea incana (Karsch)). Each PCR (20 μL total volume) consisted of 5 μL of DNA, 1 μL of each primer (10 μM), 0. 4 μL of 10 mM dNTP´s, 2. 2 μL of 25 mM MgCl2, 2 μL of 10x PCR buffer, 1 μL of bovine albumin serum (BSA), 0. 3 μL of (5 u/μL), and 7. [SEP] Justify:

Output: The text passes in relevance to the claim as it does not address the intelligence of ants or compare it to other species. Instead, it discusses a scientific procedure related to DNA analysis of various animal faecal samples, which is unrelated to the claim about the intelligence of ants.


Input: Task: corroborate the claim using the given text. Claim: AI is being used to personalize learning experiences for students by adapting to their needs and performance. [SEP] Text: Path of Science: and language COVID-19 Nigeria communication court culture discourse education enterprise judge justice language legal regulation linguistics national security social media text Home > Vol 10, No 5 (2024) > Faresta AI-Powered Education: Exploring the Potential of for Students' Needs in intelligence (AI) stands out as a relatively new yet rapidly expanding technological tool that is transforming the field of education. This paper examines the potential of to assist students and teachers in personalised learning. The research methodology employed for this study is a literature review, providing an overview of the current knowledge on practical AI applications for personalised learning and insights into methodological developments in this research field. Based on the literature, the results of this research demonstrate that personalised learning effectively accommodates students' learning p10. 22178/pos. 104-19 learning; Artificial Intelligent; Adaptive learning; Indonesia education Full Text: PDF 10. 1109/iccica52458. 2021. 9697272Alonso de Castro, M. G. , & García-Peñalvo, F. J. (2022). Metodologías educativas de éxito: proyectos Erasmus+ relacionados con e-learning o TIC. Campus Virtuales, 11(1), 95. doi: 10. 54988/cv. 2022. 1. 1022Right Commission. (2023, August 7). Prepare for an AI-Future. Retrieved from T. , Smith, L. , & Anissa, N. (2019). Educ-AI-tion Rebooted? Exploring the Future of in Schools and Colleges. Retrieved from S. K. , Farrokhnia, M. , Badali, M. , & Noroozi, O. (2021). The impacts of constructivist learning design and learning analytics on students’ engagement and self-regulation. Innovations in Education and Teaching International, 59(4), 442–452. doi: 10. 1080/14703297. 2021. 1890634Benhamdi, S. , Babouri, A. , & Chiky, R. (2016). Personalized recommender system for e-Learning environment. Education and Information Technologies, 22(4), 1455–1477. doi: 10. [SEP] Justify:

Output: The text corroborates the claim by stating that AI stands out as a rapidly expanding technological tool in education that assists students and teachers in personalized learning. It emphasizes that according to the research, personalized learning effectively accommodates students' needs, which aligns with the claim's assertion that AI is used to adapt learning experiences to students' needs and performance.


Input: Task: contrast the claim using the given text. Claim: AI assistants can guarantee complete privacy and security for all personal data and interactions. [SEP] Text: Union has led the way in legislating privacy law through the (GDPR), passed in 2016. The law requires organizations, that gather personal data about EU citizens for transactions with EU member states, must carefully protect that data to ensure privacy. In the US, the (CPRA) which expands on the 2018 (CCPA), adopts many principles from the GDPR [6]. The CCPR is designed to provide residents of California the right:to know what personal data is being collectedto know whether it is being sold or disclosed and to whomto refuse the sale of their personal datato access their personal datato request that a business delete any personal datanot to be discriminated against for exercising their privacy rights [7]At the federal level, the Act (COPRA) was introduced in December 2019 by Democratic senators, led by Maria Cantwell. Although this bill has yet to pass, and previous federal privacy bills have failed, governmental bodies continue to pursue stricter laws for governing data [8]. Privacy laws directly affect how companies operate and will require firms that use consumer data to implement systems and operational practices that enable them to conform to these new regulations. Blockchain and Technology are uniquely positioned to help companies comply with existing and potential future regulation as it relates to personal property and data privacy. Advertisement 5. Blockchain and privacyAmong the significant benefits of blockchain solutions is that they enable organizations to share data in ways not previously available, opening up possibilities for enhanced collaboration, improved operational efficiencies and expanded revenue. Questions about how to maintain privacy over the data are heightened in these environments because the data are stored in shared ledgers which may be accessible by multiple blockchain participants. ConsenSys, a blockchain technology solutions company, in discussing the security of public blockchains, argues that “In reality, privacy is not a property of any blockchain. Rather, there are layers of privacy that can be applied to any blockchain…” [9]. Designers must carefully consider which parties are allowed to read and write transactions and how transactions are broadcast, validated, and stored. Additional issues relating to how permissions and security measures are updated and enforced are also important considerations. Decisions about who owns the data and how data can be used by organizations and computer applications further complicates privacy discussions [9]. 5. [SEP] Justify:

Output: The text contrasts the claim by explaining that privacy cannot be guaranteed within digital environments, including those utilizing blockchain technology. It states, 'In reality, privacy is not a property of any blockchain. Rather, there are layers of privacy that can be applied to any blockchain,' indicating that complete privacy is not achievable and emphasizing the complexity surrounding data ownership, use, and the necessity for organizations to implement systems to comply with privacy regulations.


Input: Task: pass the claim using the given text. Claim: Ants are the most intelligent species on Earth. [SEP] Text: 1 μL of DNA-free water. PCR conditions were as follows: initial denaturation at 94°C for 3. 5 min; 38 cycles of 94°C for 1 min, 44°C for 1 min (annealing temperature), 72°C for 1. 5 min; a final extension at 72°C for 7 min. PCR products were detected by electrophoresis in 2% GoldView-stained agarose gels. Amplified products were purified using the (Quiagen) and sequenced in both directions with Bigv3. 1 (Applied Biosystems). Sequencing was carried out on an ABI Prism 3130 (Applied Biosystems). Sequences were assembled in Sequencher 4. 8 () and aligned using ClustalW (Thompson et al. , 1994) implemented in MEGA 5. 1 (Tamura et al. , 2011). Based on sequence similarities/differences, group-specific primers, which would amplify DNA of mimics in the predator guts, were designed using Amplicon. b08 (Jarman, 2004). Four primer pairs were tested with all prey and predator specimens used in this study to ensure that the primers amplify DNA of the studied prey groups only: primers MyrmF and MyrmR amplified Myrmarachne spiders, DaerF and DaerR amplified Daerlac true bugs, FormF and FormR amplified Camponotus, Polyrhachis and Myrmecia ants, and BothmF and BothmR primers amplified Bothriomutilla mutillid wasps (for primer sequences see Table 4) and to reject cross-amplifications with the predators. The sequences of cytochrome c oxidase obtained from the studied predators and the mimics are deposited in GenBank (Accession numbers KX980351--KX980391). Table 4 List of primer pairs used to identify mimics in the gut or faeces of potential predators. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses PCR procedures and primer sequences related to DNA analysis without mentioning ants or intelligence. There is no connection to the intelligence of any species, making this text not pertinent to the assertion about ants.


Input: Task: corroborate the claim using the given text. Claim: AI is being used to personalize learning experiences for students by adapting to their needs and performance. [SEP] Text: Context-aware adaptive and personalized mobile learning delivery supported by UoLmP. Journal of University - Computer and Information Sciences, 26(1), 47–61. doi: 10. 1016/j. jksuci. 2013. 10. 008Guan, C. , Mou, J. , & Jiang, Z. (2020). Artificial intelligence innovation in education: A twenty-year data-driven historical analysis. of Innovation Studies, 4(4), 134–147. doi: 10. 1016/j. ijis. 2020. 09. 001Gunawan, K. D. H. , Liliasari, L. , Kaniawati, I. , & Setiawan, W. (2021). Implementation of Program for Assisted by in Designing HOTS-based Learning. 7(1), 55. doi: 10. 30870/jppi. v7i1. 8655Hasanov, A. , Laine, T. H. , & Chung, T. -S. (2019). A survey of adaptive context-aware learning environments. Journal of 11(5), 403–428. doi: 10. 3233/ais-190534Heffernan, N. T. , & Heffernan, C. L. (2014). : Building a Platform that for Research on and Teaching. of in Education, 24(4), 470–497. doi: 10. 1007/s40593-014-0024-xHolonIQ. (2023, February 27). in Education. 2023 Survey Insights. Retrieved from C. -W. , & Chen, S. Y. (2016). A Perspective to Handheld Devices: Customization vs. Personalization. Review of Research in Open and Distributed Learning, 17(1). doi: 10. 19173/irrodl. v17i1. 2168Hssina, B. , & Erritali, M. (2019). A on a in an System. Science, 151, 1152–1157. doi: 10. 1016/j. procs. 2019. 04. 164Hu, J. (2021). System by use of Methods. of in Learning, 16(05), 87. doi: 10. 3991/ijet. v16i05. 20299Huang, A. Y. [SEP] Justify:

Output: The text references various studies and analyses that discuss the implementation of artificial intelligence in educational contexts, particularly focusing on adaptive and personalized learning environments. This supports the claim by implying that AI technologies are indeed being utilized to tailor learning experiences based on individual student needs.


Input: Task: contrast the claim using the given text. Claim: AI assistants can guarantee complete privacy and security for all personal data and interactions. [SEP] Text: 1 Decentralized identitySelf-sovereign identity, a widely held view among blockchain proponents, holds that individuals should have control over their own identities and should have autonomy over how facets of identity are shared with others. Decentralized identity (DID) is a blockchain-enabled embodiment of self-sovereign identity that can profoundly improve the privacy and security of personal data. DID refers to individual ownership of personal digital data relating to many elements of identity. Microsoft, which participates in defining DID standards, takes the perspective of the individual. “Currently, our identity and all our digital interactions are owned and controlled by other parties, some of whom we aren’t even aware of [10]. ” Returning ownership of data to the individuals to whom the data pertains can provide benefits both to those individuals and to organizations that would otherwise be responsible for protecting the data. Blockchain technology enables DID and provides a way for individuals to store their own data outside of the databases of the parties with whom they transact. Data are owned and controlled by these individuals and pointers to this data or metadata can be stored on the blockchain and can be used to verify the validity of claims the users make about their personal data. For example, a driver’s license bureau might issue a driver’s license to a user, which the user stores privately. When an insurance company or other party wishes to verify that the user is licensed, the user can present the license to a party such as an insurance company, and the party can independently verify the issuer and expiration date. Anyone can create a DID. When this identity is first created, there is no information attached to it. Over time, the user could attach a driver’s license or other identifying data to that DID. The process that a third party might use to verify that a particular person owns a DID, is similar to the process of validating that a person owns an email address. For example, an online gaming account can be attached to an email address. A party seeking to validate that a person was the owner of that account could send a private message, such as a security code, to the email address and ask the person to provide that code, something that only the person possessing the password for that email address could provide. Unlike an email account, the DID would be owned and stored by a person rather than by an email service provider. [SEP] Justify:

Output: The text contrasts the claim by explaining the concept of decentralized identity (DID) and how it lets individuals control their own identities and associated personal data. It states that currently, personal data and digital interactions are owned and controlled by other parties, indicating that complete privacy and security cannot be guaranteed without changing who controls the data. The emphasis on individuals returning ownership of their data highlights that while improved privacy and security can be facilitated, the absolute guarantee claimed in the statement is not achievable under current systems.


Input: Task: pass the claim using the given text. Claim: Ants are the most intelligent species on Earth. [SEP] Text: PrimerSequenceFragment length (bp)Amplified generaDaerF5´- −3´204Daerlac DaerR5´- −3´FormF5´- −3´220Camponotus, Myrmecia, Polyrhachis FormR5´- −3´MyrmF5´- −3´142Myrmarachne MyrmR5´- −3´BothmF5´- −3´215Bothriomutilla BothmR5´- −3´ DNA was extracted from 553 spider abdomens using D (Qiagen) following the manufacturer’s animal tissue protocol with a change in final step when only 50 μL of elution buffer was used. DNA from fecal samples (50 samples of skinks and 48 samples of noisy miner) was extracted using the DNeasy mericon (Qiagen) following manufacturer’s protocol. PCRs were performed using Multiplex PCR kit (Qiagen) under the following conditions: initial denaturation at 95°C for 15 min; 42 cycles of 94°C for 30 s, annealing temperature for 90 s (47. 5°C when using Bothm primers, 43 and 45°C with the other primers), 72°C for 90 s; and a final extension at 72°C for 10 min. The reaction mixture total volume of 20 μL consisted of 10. 6 μL of 1. 8 μL of Q-Solution, 2. 1 μL of RNase-free water, 0. 5 μL of 10 μM forward and 0. 5 μL of reverse primers, and 4. 5 μL of DNA. PCR with Bothm primers was done separately whereas three remaining primer pairs were used in one multiplex reaction. PCR products were detected by electrophoresis in 2% GoodView-stained agarose gels. PCR was repeated for each sample and primer combination at least three times to exclude falsely negative results. In positive samples, PCR was repeated once again using primers modified with MID identifiers (i. e. multiplex identifiers, 10bp-long barcoding sequences). Each sample was thus marked with a unique combination of barcodes. PCR products were purified using a (Qiagen) according to the manufacturer’s protocols. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses a scientific protocol for DNA extraction and PCR methods without any mention or analysis of ant intelligence or comparison to other species.


Input: Task: corroborate the claim using the given text. Claim: AI is being used to personalize learning experiences for students by adapting to their needs and performance. [SEP] Text: Q. , Lu, O. H. T. , & Yang, S. J. H. (2023). Effects of artificial Intelligence–Enabled personalized recommendations on learners’ learning engagement, motivation, and outcomes in a flipped classroom. Computers & Education, 194, 104684. doi: 10. 1016/j. compedu. 2022. 104684& Chen, N. -S. (2012). Empowering personalized learning with an interactive e-book learning system for elementary school students. Research and Development, 60(4), 703–722. doi: 10. 1007/s11423-012-9237-6Hwang, G. -J. (2014). Definition, framework and research issues of smart learning environments - a context-aware ubiquitous learning perspective. Environments, 1(1). doi: 10. 1186/s40561-014-0004-5Hwang, G. -J. , Xie, H. , Wah, B. W. , & Gašević, D. (2020). Vision, challenges, roles and research issues of in Education. Computers and Education: Artificial Intelligence, 1, 100001. doi: 10. 1016/j. caeai. 2020. 100001Jaiswal, A. , & Arun, C. J. (2021). Potential of for transformation of the education system in India. of Education and Development using Information and (), 17(1), 142-158. Jung, E. , Kim, D. , Yoon, M. , Park, S. , & Oakley, B. (2019). The influence of instructional design on learner control, sense of achievement, and perceived effectiveness in a supersize MOOC course. Computers & Education, 128, 377–388. doi: 10. 1016/j. compedu. 2018. 10. 001Kabudi, T. , Pappas, I. , & Olsen, D. H. (2021). AI-enabled adaptive learning systems: A systematic mapping of the literature. Computers and Education: Artificial Intelligence, 2, 100017. doi: 10. 1016/j. caeai. 2021. 100017Karaoglan Yilmaz, F. G. , & Yilmaz, R. (2021). Students’ Engagement in Online Learning. [SEP] Justify:

Output: The text refers to various studies and papers that explore the effects of artificial intelligence (AI) in educational settings, specifically mentioning 'AI-enabled adaptive learning systems' and 'personalized recommendations.' This supports the claim that AI is being used to personalize learning by adapting to student needs and performance, as the text discusses improvements in learner engagement, motivation, and outcomes, which are key aspects of personalized learning.


Input: Task: contrast the claim using the given text. Claim: AI assistants can guarantee complete privacy and security for all personal data and interactions. [SEP] Text: The password, or private key, would also be secured by the owner. Personal information relating to the identity could be stored in an identity hub—an encrypted repository of personal data that is stored outside the blockchain, likely in a combination of phone, PC, and cloud data or offline storage devices [10]. Through the use of an identity hub, the person could control which pieces of information to share with an external party. DIDs reduce the probability of unwanted correlation. The use of common identifiers—such as email addresses on different web sites—creates what is called a correlation problem. Correlation in this context means entities can, without a user’s consent, associate information about a single identity across multiple systems. Email addresses utilize data on almost every website. When users provide the same email address on different sites—along with perhaps additional pieces of personal information like a phone number or physical address—they unknowingly enable a potential for correlation. In this case, entities can correlate that data across sites. Tracking cookies and web clicks enable the linking of IDs across websites which can result in outsiders gaining a full picture of users’ identity, where they live, their gender, age range, interests, and other information [10]. Figure 2 depicts how a user of several services and on-line websites can store data in a central user-controlled location and interact separately with each service provider. This enables the user to control the specific pieces of information that can be seen by each provider. Figure 2. Decentralized identities and service providers. 5. 2 Blockchain-enabled federated identityDIDs can help users secure and control their data property and determine who gets access to that data. Blockchains can also increase security for individuals when interacting with multiple internet platforms or services through the use of decentralized federated identities. Blockchains allow entities to protect privacy of individuals—central to self-sovereign identity. Traditionally, users of a system or set of systems possess what is referred to as a federated identity, which can be described as a single identity used by individuals to access services or information platforms, provided by multiple parties, whereby a single identity is enabled and determined by single sign on (SSO) authentication. Consider a health care network that includes multiple entities like hospitals, insurance carriers, or urgent care clinics, where the providers enable the use of a single sign-on credential or digital federated identity to access all services. [SEP] Justify:

Output: The text contradicts the claim by discussing the potential vulnerabilities associated with personal data. It explains that even with secure methods like identity hubs and decentralized identities, there are risks, such as the correlation problem, where entities can link a user's information across different platforms without consent. This indicates that complete privacy and security cannot be guaranteed, as there are multiple ways that personal data can be exposed or correlated, thus undermining the claim.


Input: Task: pass the claim using the given text. Claim: Ants are the most intelligent species on Earth. [SEP] Text: The concentration of each PCR product was measured using NanoDrop 8000 UV-(Thermo Scientific, Waltman, MA) and assessed comparing to 50 bp ladder (Fermentas). Five μL of 50 μg/μL PCR products was pooled into the same sterile vial and sent for sequencing. Sequencing library was prepared using Kit and 2 200 Kit v3 (Scientific). Sequencing on the System was provided by the SEQme company (Dobříš, Czech Republic). Sequencing data were processed using the Galaxy platform ( BioEdit 7. 2. 5 (Hall, 1999) and MEGA 5. 10. Reads were split according to their MIDs resulting in files corresponding to individual predators. Then, forward and reverse primers were removed and sequences were filtered according to their length (<120 or 180 bp) to remove dimers or too short reads. The sequences were collapsed and rare haplotypes (containing <2 identical sequences) were removed. Furthermore, sequences with stop codons and indels causing frameshifts were also removed. Remaining haplotypes were clustered into molecular operational taxonomic units (MOTU) using jMOTU 4. 1 (Jones et al. , 2011) with a 4 bp cut-off (corresponding to 3% sequence divergence). Each MOTU was compared to the GenBank database using megablast and BOLD database and to the sequences of mimics obtained within this study. NGS produced 11 068 904 informative reads from all predators of which 614,273 reads belonged to the mimics. Availability of mimics to the predators was estimated by visual search on the trunks and below bark of 20 gum trees randomly selected on the campus. On each tree, abundance of mimics was surveyed by one person up to the height of 2 m from the ground for a period of 30 min on a sunny day. All mimics were collected, brought to the laboratory and identified to species. To test a hypothesis that the frequencies of captured mimics by all predators and those available at the microhabitat of predators are similar we used Chi-square test. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses laboratory techniques, data processing, and hypotheses testing in ecological research without mentioning ants or intelligence.


Input: Task: corroborate the claim using the given text. Claim: AI is being used to personalize learning experiences for students by adapting to their needs and performance. [SEP] Text: Technology, Knowledge and Learning, 27(2), 449–460. doi: 10. 1007/s10758-021-09547-wKatuk, N. , Kim, J. , & Ryu, H. (2013). Experience beyond knowledge: Pragmatic e-learning systems design with learning experience. Computers in Human Behavior, 29(3), 747–758. doi: 10. 1016/j. chb. 2012. 12. 014Khan, I. , Ahmad, A. R. , Jabeur, N. , & Mahdi, M. N. (2021). An artificial intelligence approach to monitor student performance and devise preventive measures. Environments, 8(1). doi: 10. 1186/s40561-021-00161-yKim, H. -S. , Kim, N. Y. , & Cha, Y. (2021). Beneficial to Use AI Chatbots to Improve Learners’ Speaking Performance? of AsiaTEFL, 18(1), 161–178. doi: 10. 18823/asiatefl. 2021. 18. 1. 10. 161Kitchenham, B. (2004). Procedures for performing systematic reviews. Retrieved from D. , Huh, Y. , Lin, C. -Y. , & Reigeluth, C. M. (2018). Technology functions for personalized learning in learner-centered schools. Research and Development, 66(5), 1269–1302. doi: 10. 1007/s11423-018-9615-9Li, S. , & Gu, X. (2023). A for Human-Intelligence in Education: Based on and Delphi-AHP Method. & Society, 26(1), 187–202. Lin, C. F. , Yeh, Y. , Hung, Y. H. , & Chang, R. I. (2013). Data mining for providing a personalized learning path in creativity: An application of decision trees. Computers & Education, 68, 199–210. doi: 10. 1016/j. compedu. 2013. 05. 009Luckin, R. , & Cukurova, M. (2019). Designing educational technologies in the age of AI: A learning sciences‐driven approach. of Educational Technology, 50(6), 2824–2838. [SEP] Justify:

Output: The text corroborates the claim by referencing various studies that discuss the application of AI in education settings, particularly in personalizing learning experiences. For instance, it mentions the use of AI to monitor student performance and devise preventive measures, which aligns with the idea of adapting educational content based on individual needs. Additionally, it discusses technology functions for personalized learning, reinforcing the claim that AI is employed to tailor learning experiences.


Input: Task: contrast the claim using the given text. Claim: AI assistants can guarantee complete privacy and security for all personal data and interactions. [SEP] Text: This type of identity, which is typically stored and managed in a central location by a service provider, is prone to security vulnerabilities [11]. The distributed nature of Blockchain technology provides an opportunity for networks to enable single sign-on, or federated identities much more securely. ElGayyar [11] proposes a blockchain-based federated identity framework (BFID) where the network of providers themselves, rather than a centralized third party, manage the system, identification, and authentication of the users. Any entity within the blockchain network can verify credentials and issue the identity for any user in the system. In a BFID, all transactions are written and maintained within the blockchain where the system takes advantage of the secure and immutable nature of the distributed ledger, thereby practically eliminating the possibility for identity breaches and potential theft. Blockchain-based federated identity frameworks can be configured on both public and private blockchain implementations and make use of smart contracts to react to potential rule changes that may occur while governing identity management within the system. Additionally, these frameworks enable users to audit and control how their identities are used while also providing the network business entities the ability to monitor how their services are being used, enabling process improvement and a better overall user experience. 5. 3 Zero-knowledge proofsZero-knowledge proofs enable ease of access to identity and other important data while maintaining privacy and property control for individuals. Zero-knowledge proofs are cryptographic methods whereby a user or “prover” can convince someone, or a “verifier” that something about them is true without providing, revealing or sharing that information. A common example is a customer attempting to order an alcoholic beverage from a bartender who demands to know that the patron is 21 of age or older. Providing a driver’s license reveals the patron’s full birth date as well as height, eye color, and home address—information that could be misused or stolen. Zero-knowledge proofs use cryptographic algorithms that enable a prover to mathematically demonstrate to a verifier that a statement is correct without revealing any data. When the state issues a 21-and-over driver’s license, it asks the driver to type in a secret nickname, unknown to the licensing bureau. This nickname could then be hashed together with the driver’s license number, and stored in a public list representing valid drivers over 21. [SEP] Justify:

Output: The text contrasts the claim by highlighting the security vulnerabilities inherent in centralized systems, suggesting that complete privacy and security cannot be guaranteed. Instead, it discusses the advantages of a blockchain-based federated identity framework which aims to enhance security and privacy through distributed management, demonstrating that while improved security measures can be implemented, complete assurance of privacy and security is not attainable.


Input: Task: pass the claim using the given text. Claim: Ants are the most intelligent species on Earth. [SEP] Text: In order to estimate the attack rate exerted by the whole community of predators as precisely as possible, we had to take into account two important variables that affect the predation: (1) the relative frequency (fij) of positively screened ith predator individual for each jth mimic; (2) the capture probability of araneophagous spider predator (Lampona), where body size is a strong predictor of the likelihood of predation. The capture probability was estimated for each spider predator and mimic (Pekár et al. , 2016). This latter adjustment was necessary in order to take into account the fact that spiders could have captured juvenile Myrmarachne or Daerlac individuals, which are not golden mimics and could not be distinguished from adult specimens by molecular methods. The functional model for the capture probability was obtained from data on eight spider species representing different guilds (Nentwig and Wissel, 1986). The logistic function was 11+exp(−1. 99+2. 11ratioij), where ratioij is the mimic/predator body size ratio. The ratio was estimated by measuring the body size of ten collected specimens of each predator and mimic. Then the overall pressure by the community of predators was estimated as a sum of all attack rates by means of the killing value, Kj (Varley and Gradwell, 1960): Kj=∑j=1n−log(1−fij). To test whether natural mortality caused by predation is related to unpalatability of mimics we used nonparametric regression as the response variable had non-standard distribution, namely generalized additive model (GAM) from the mgcv package (Wood, 2011). We regressed log-transformed K-values (in order to stay within positive bounds) against unpalatability index. Trials with selected predators Request a detailed protocol For predation trials we selected five species of mimics found on the campus. The five species represented a gradient of unpalatability (Table 3) within the mimetic complex. We included examples of the most unpalatable to the least palatable mimics: the ants Polyrhachis ammon, Polyrhachis vermiculosa, and Camponotus aeneopilosus, the true bug Daerlac nigricans, and the spider Myrmarachne luctuosa. [SEP] Justify:

Output: The text is irrelevant to the claim about ants being the most intelligent species on Earth. It discusses predation variables and model estimates related to predator species and mimics, mentioning certain ant species in the context of palatability but does not provide any information about the intelligence of ants or compare it to other species.


Input: Task: corroborate the claim using the given text. Claim: AI is being used to personalize learning experiences for students by adapting to their needs and performance. [SEP] Text: doi: 10. 1111/bjet. 12861Luckin, R. , Holmes, W. , Griffiths, M. , & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Retrieved from O. , & Lutsenko, G. (Eds. ). (2022). - Theory and Practice. doi: 10. 5772/intechopen. 87353Matari, A. (2023). and Future of Teaching and Learning. Retrieved from A. (2019). Emotional AI and EdTech: serving the public good? Learning, Media and Technology, 45(3), 270–283. doi: 10. 1080/17439884. 2020. 1686016Microsoft. (2021, March). Understanding the Landscape: Analytics and AI in Education. Retrieved from & Bettayeb, A. M. (2016). University Instructors’ to Management Systems: System Case. Proceedings of the 18th on Systems. doi: 10. 5220/0005748102150222Nedungadi, P. , & Raman, R. (2012). A new approach to personalization: integrating e-learning and m-learning. Research and Development, 60(4), 659–678. doi: 10. 1007/s11423-012-9250-9Newman, M. , & Gough, D. (2019). in Educational Research: Methodology, Perspectives and Application. in Educational Research, 3–22. doi: 10. 1007/978-3-658-27602-7_1Ouyang, F. , Wu, M. , Zheng, L. , Zhang, L. , & Jiao, P. (2023). Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course. of in Higher Education, 20(1). doi: 10. 1186/s41239-022-00372-4Pataranutaporn, P. , Danry, V. , Leong, J. , Punpongsanon, P. , Novy, D. , Maes, P. , & Sra, M. (2021). AI-generated characters for supporting personalized learning and well-being. Intelligence, 3(12), 1013–1022. [SEP] Justify:

Output: The text corroborates the claim by discussing the integration of artificial intelligence in educational contexts, specifically mentioning 'AI-generated characters for supporting personalized learning and well-being.' This indicates that AI is actively being used to tailor learning experiences for students, aligning with the claim that AI adapts to their needs and performance.


Input: Task: contrast the claim using the given text. Claim: AI assistants can guarantee complete privacy and security for all personal data and interactions. [SEP] Text: At the bar, you could type your nickname and license number into a hash generator, and if the resulting hash matched one on the list, the bartender would know that you were of legal age [12]. There are two types of zero knowledge proofs, interactive and non-interactive. Most commonly, zero knowledge protocols are interactive whereby the prover (an individual or more likely a computer) and the verifier participate in a back and forth set of questions or challenges that, when answered correctly a given number of times, enables the prover to convince the verifier, with very high probability, that the statement they are making is true. An example of an interactive zero knowledge proof could involve two colored balls that are identical in every way accept their color. One is red and one green. Let’s assume the verifier is completely color blind and cannot tell the color of either ball. You want to prove to the verifier that the balls do in fact differ in color. The verifier puts the balls behind their back and shows one. The prover indicates the color. The verifier then does this again and asks if they switched the ball. Since you can see the different colors you can say with certainty that the ball was either switched or not. After several rounds of this, it becomes more statistically true that there are in fact two different balls as the probability that you could guess correctly over and over goes down to almost zero [13]. Non-interactive proof is more like the example above of the patron proving their age to a bartender with a proof statement that reveals age but not additional information that might be revealed if the prover were to show their photo. Proving which point Value a card in a deck of 52 cards, without identifying its suit, can provide an example of this type of proof. The prover states that the card they are holding is a king but does not want to reveal which king—the king of hearts, diamonds, spades or clubs. If the cryptographic string also contains information that reveals the other 48 cards, none of which are kings, we can know for certain that the prover does in fact hold a king of some kind. Zero knowledge proofs are powerful tools for maintaining privacy and property control for individuals that may need to provide a bit of personal information but no more than absolutely necessary. Advertisement 6. [SEP] Justify:

Output: The text contrasts the claim by discussing the concept of zero knowledge proofs, which allow for the verification of information without revealing additional personal data. While zero knowledge proofs can enhance privacy, they do not guarantee complete privacy and security, as the text implies that individuals still need to provide some personal information to prove certain claims. Therefore, the claim that AI assistants can guarantee complete privacy and security is not supported by the explanation of how zero knowledge proofs function.


Input: Task: pass the claim using the given text. Claim: Ants are the most intelligent species on Earth. [SEP] Text: Based on their unpalatability, the ants and bugs were predicted to be Müllerian mimics, while the spider was predicted to be a Batesian mimic. Unpalatability was re-estimated for these five species using ten individuals from each species. All attributes as measured previously were re-measured with the addition of a behavioural measure. The behavioural measure was the frequency of biting in the field following agitation with forceps. The addition of an extra measure adjusted the previous estimated unpalatability (uj) but the relative relationship of unpalatability between species remained the same. We used Eulamprus quoyii as our visually-oriented euryphagous vertebrate predator. At least 14 days prior to the experiment adult skinks were placed singly in a white container (70 × 50 × 40 cm) at constant temperature 26°C and L:D = 12:12 regime. They were fed regularly with three crickets (Acheta domestica) three times a week. The skinks were then starved five days prior to the predation trials. One day prior to the trial a layer of butter was applied to the sides of the white container to prevent arthropods from climbing the walls and escaping. At the start of a trial a lizard was left to settle down for a period of 5 min. A prey item was released into the container and the behaviour of the skink during the 5 min trial was recorded with a fixed video camera positioned above the container. Five minutes was deemed sufficient, as previous feeding trials with crickets have shown this species of lizard will attempt to capture prey within 1 min. If the potential prey survived it was removed, and the skink was left for 2 min to settle down. Another prey item was then released and so on until all five mimic prey types were used. As a non-mimic control, we used juveniles of the spider, Badumna insignis L. Koch, which was of a similar size to the other species but was not a mimic as it lacked golden colouration. At the end a cricket was released as a positive control. Results of trials were used in the analysis only if the cricket was captured. The prey was offered to skinks using a constrained randomisation. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses a study on the behavior and mimicry of various species, including ants and skinks, without making any reference to intelligence or comparing the intelligence of ants with that of other species. Therefore, it does not corroborate or contrast the claim.


Input: Task: corroborate the claim using the given text. Claim: AI is being used to personalize learning experiences for students by adapting to their needs and performance. [SEP] Text: doi: 10. 1038/s42256-021-00417-9Pelletier, K. , McCormack, M. , Reeves, J. , Robert, J. , Arbino, N. , Al-Freih, W. M. , Dickson-Deane, C. , Guevara, C. , Koster, L. , Sanchez-Mendiola, M. , Skallerup Bessette, L. , & Stine, J. (2022). 2022. Teaching and Learning Edition. Retrieved from K. ,. , Vens, C. , & Van den Noortgate, W. (2019). Integrating machine learning into item response theory for addressing the cold start problem in adaptive learning systems. Computers & Education, 137, 91–103. doi: 10. 1016/j. compedu. 2019. 04. 009Pontual Falcão, T. , Mendes de Andrade e Peres, F. , Sales de Morais, D. C. , & da Silva Oliveira, G. (2018). Participatory methodologies to promote student engagement in the development of educational digital games. Computers Education, 116, 161–175. doi: 10. 1016/j. compedu. 2017. 09. 006Prasetiyo, W. H. , Naidu, N. B. M. , Tan, B. P. , & Sumardjoko, B. (2021). Trend in Educational Sphere: A Systematic Review. of Evaluation and Research in Education, 10(4), 1192-1201. Ramos de Melo, F. , Flôres, E. L. , Diniz de Carvalho, S. , Gonçalves de Teixeira, R. A. , Batista Loja, L. F. , & de Sousa Gomide, R. (2014). Computational organization of didactic contents for personalized virtual learning environments. Computers & Education, 79, 126–137. doi: 10. 1016/j. compedu. 2014. 07. 012Rodway, P. , & Schepman, A. (2023). The impact of adopting AI educational technologies on projected course satisfaction in university students. Computers and Education: Artificial Intelligence, 5, 100150. [SEP] Justify:

Output: The text corroborates the claim by mentioning various studies related to the integration of machine learning and AI in educational contexts. For example, it references work on 'integrating machine learning into item response theory for addressing the cold start problem in adaptive learning systems,' which implies that AI technologies are being used to tailor learning experiences to the individual needs and performances of students.


Input: Task: contrast the claim using the given text. Claim: AI assistants can guarantee complete privacy and security for all personal data and interactions. [SEP] Text: Artificial intelligence and privacy(AI) is a broad field that includes machine learning and cognitive computing where computers are programed to mimic human cognitive functions such as learning and problem solving but many times, much faster and in more accurate ways [14]. The use of AI is expanding into a plethora of areas including speech recognition, facial recognition, medical diagnosis, financial predictions, tracking of disease outbreaks etc. AI algorithms enable computing systems to rationalize and take actions aimed at achieving a specific goal or set of goals. User and stakeholder security can be enhanced through AI tools, which can take advantage of blockchain to open up new avenues for accessing and learning from data without taking ownership or control of that data. This can reduce risk for the organization and the stakeholders who provide the data. Both individual blockchain members and the organization or group in charge of setting governance rules and processes can benefit from building in privacy-related AI functionality (as early as possible) in the design of blockchain networks and processes. Companies have implemented AI to create holistic views of customers by piecing together transactions from all customer touchpoints. Blockchain participants will have incentives to pull together integrated datasets by combining transactions for a single customer across all blockchain partners. This creates potential benefits for blockchain partners, but can also negatively affect the privacy of customers and other stakeholders for which this integration is possible. In combination with options for identity protection through decentralization, AI can be used to combine personal data from blockchain participants and their stakeholders in a way that maintains information security and personal data privacy. Through these processes, user and stakeholder security can be enhanced and data sets and AI models can be improved. We can identify four categories of stakeholders that can be affected by an organization’s data transparency and privacy processes: (1) participants, whose data—both direct and indirect—are gathered; (2) victims, who are affected by decisions made using participant data; (3) users, who use participant data in their work; and (4) custodians, who manage and secure data. When AI can be used to manage access to data and to develop analytical models using that data, all stakeholders can benefit [15]. Table 1 summarizes a number of ways AI can be used in a blockchain setting to protect or increase privacy of user’s personal data. [SEP] Justify:

Output: The text contrasts the claim by outlining that while AI can enhance security and privacy in various contexts, it also points out potential risks. It mentions that integrating data from multiple sources can negatively affect customers' privacy, stating 'This creates potential benefits for blockchain partners, but can also negatively affect the privacy of customers and other stakeholders.' This indicates that complete privacy and security cannot be guaranteed.


Input: Task: pass the claim using the given text. Claim: Ants are the most intelligent species on Earth. [SEP] Text: There was randomisation within Müllerian mimics (ants) followed by randomisation within less defended mimics (Daerlac, Myrmarachne) and non-mimic prey (Badumna) because the predator must learn unpalatability by encountering the unpalatable models first. The learned aversion was necessary because skinks were collected in the wild a number of months prior to the trials and fed with crickets since then. We used 26 skinks in total. Ants, spiders and bugs were collected a few days prior to the trial. From the video footage we recorded whether prey was consumed or spat out. The spider Lampona murina L. Koch was used as a nonvisually-oriented ant-adverse predator that will not capture ants but other spiders. Twenty-five late-instar juvenile Lampona spiders were collected from the bark of gum (Eucalyptus sp. ) trees on the campus and kept individually in tubes (1 cm diameter, 10 cm long). Spiders were placed in a chamber set to 23°C and L:D = 16:8 regime. Spiders were fed with a clubionid spider once a week and provided with a moistened piece of gauze. Lampona was not fed seven days prior to a trial. During a trial, an individual from the five mimetic prey species and one non-mimic spider (Clubiona sp. ) was released into the Petri dish in a random order (diameter 6 cm). The prey was offered to Lampona in a random order. Induction of learned aversion was not necessary because the spiders were collected from the field only a week prior to the experiment. If the prey was not captured within 30 min we replaced it with a new one until one prey was captured. If the prey was captured, another prey was offered seven days later. In each trial we recorded capture frequency of Lampona. Finally, we used the spider Servaea incana (Keyserling) as a specialised ant-eating predator. Twenty-seven adult individuals of Servaea spiders were collected from gum trees on the campus and placed singly in a Petri dish (8 cm diameter) and kept at 23°C and L:D = 16:8. Spiders were fed with small crickets once a week and provided with a moistened piece of gauze. We did not feed Servaea for five days prior to a trial. [SEP] Justify:

Output: The text does not address the claim about the intelligence of ants. Instead, it describes an experimental setup involving various species, including ants, in a predator-prey context without making any assertions about ant intelligence or comparing it to that of other species. Thus, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: The majority of AI tools in education focus on automating administrative tasks like grading and attendance. [SEP] Text: The GP skim-read the letter on her monitor and after a few minutes declared, ‘ah hah, you see here it is’, highlighting a paragraph with the mouse cursor, saying ‘this is all I need, out of this whole document, this is what is relevant to me. ’ She continues: ‘I can tell the rest of this letter is for their [the specialist’s office] purposes, it’s a form of documentation, I don’t need all this’. The GP did not require most of the content of the letter, only a single paragraph. Her prior experience of the specialist who wrote the letter enabled her to extract the relevant information efficiently and discount the rest. Documentation of clinical consultationsA scenario in which a system could automate clinical documentation using natural language processing and text summarisation was described to staff in focus groups and interviews, provoking lengthy discussion. Some participants saw it as a potentially useful technology, with multiple ‘when can we have that’ comments, while others felt it might have negative consequences on the clinical encounter and could even undermine the skills and critical thinking of the clinician. One GP argued that having a transcript of the entire conversation would be a waste of time, producing a great deal of unnecessary information. It is not uncommon for GPs to rewrite or edit their notes, particularly notes for new cases or complex circumstances, or when the notes are written by trainees. What may look like a relatively simple administrative task in fact involves a considerable amount of clinical skill and experience. Removing this task from the clinical workflow would remove an opportunity for the clinician to reflect critically about an interaction. Research shows that writing engages the brain, allows GPs to be better observers, supports empathy and engages critical thinking. 34Scope for automationThe online survey of experts in automation systems achieved 156 responses from academic and industrial experts around the world, providing a total of 4599 task level ratings. The distribution of respondents by country and qualification is shown in figures 2 and 3. Process machine learning model that we applied to the data suggests that about 44% of the administrative tasks performed in primary care are ‘mostly’ or ‘completely’ automatable using current technology. Figure 4 shows the distribution of automatability for all administrative tasks performed by general practice staff. Download figure Open in new tab Download powerpoint Figure 4 Distribution of automatability scores. [SEP] Justify:

Output: The text discusses the potential for automation in administrative tasks within the clinical setting, specifically mentioning that about 44% of administrative tasks in primary care could be 'mostly' or 'completely' automatable using current technology. While the specific context is healthcare rather than education, the underlying theme of automation of administrative tasks supports the claim that AI tools, in general, are utilized to automate such tasks, which can also apply to the educational sector.


Input: Task: contrast the claim using the given text. Claim: AI assistants can guarantee complete privacy and security for all personal data and interactions. [SEP] Text: This AI/Blockchain combination can increase system security by helping to detect attacks by bad actors, user security by sharing permissions and smart contracts, enable privacy-enhanced use of datasets through improved identity management and better data, and it can improve AI models through more varied, valid, and ethically-sourced data and better hypotheses. Each item in Table 1 is described briefly below, followed by examples of use cases using this combination of technologies. System SecurityMalicious attack detectionMore robust cryptographic functionalityUser SecurityUsers decide what data to shareSmart contracts can enforce established permissionsDatasetsImproved identity masking and metadataCleaner and more accurate dataAI ModelsBroader scope and greater variety of dataImproved validity of data and modelsEthically sourced and permissionedCareful construction of hypothesesTable 1. The role of AI in blockchain user privacy. Computational intelligence (CI), a subset of AI can improve the Blockchain’s attack resilience thus improving security of the system and ultimately the privacy of the data residing on the system. AI is rooted in hard computing techniques whereas computational intelligence is based on soft computing methods, which enable adaptation to a range of changing variables [16]. Computational intelligence, when combined with blockchain systems, can create more robust cryptographic functionality and ciphers thereby making it more difficult for cyber hackers to compromise systems even as computing power and efforts to hack these systems over time increases. Quite appropriately, [14] refer to the intersection of blockchain and AI as “blockchain intelligence”. Additionally, AI algorithms can be built on blockchains to detect when a blockchain is under attack by continually monitoring blocks and activity on the chain. This technology increases trust in the system beyond what the native architecture provides [17, 18]. When blockchain participants have increased control over their own data, they have the potential to decide with which parties and for what purposes their data are shared. In order to collect participant data for use in an AI dataset, participant permissions will need to be obtained. This provides users with ‘opt-in’ control, rather than ‘opt-out’ and helps to ensure that personal data is used in ways that are consistent with the intentions of the owner. In some cases, which may become increasingly common if decentralized identity solutions are adopted, users can be compensated for providing their data to organizations seeking to utilize this data in traditional and AI decision models. Smart contracts can also protect privacy. [SEP] Justify:

Output: The text contrasts the claim by highlighting that while AI and blockchain technologies can enhance security and privacy features, they do not guarantee complete privacy and security for personal data. For instance, it mentions that users have control over what data to share and how it is used, but this implies that privacy depends on user choice and involvement, which does not equate to a guarantee of privacy. Furthermore, it describes how the combination of AI and blockchain improves security and facilitates user permissions, but it also acknowledges the presence of risks such as attacks by bad actors, thus demonstrating that absolute security is not assured.


Input: Task: pass the claim using the given text. Claim: Ants are the most intelligent species on Earth. [SEP] Text: One individual of a mimic prey species was released into the dish. The prey was offered to Servaea in a random order. Induction of learned aversion was not necessary because spiders were collected from the field only a week prior to the experiment. If the prey was not captured within 5 min we replaced it with a new one. If the prey was captured another prey was offered five days later. In each trial we recorded whether the prey was captured as well as the behaviour of Servaea and prey. To compare the frequency of capture among used prey types for each predator, relative frequencies of prey capture from Eulamprus quoyi or Servaea experiments were subjected to generalised estimating equations (GEE) from the geepack package with a binomial error structure due to repeated use of the same skink individuals with different prey. The exchangeable association structure was used (Varley and Gradwell, 1960). To test whether the post-attack reaction of the skinks was related to prey unpalatability we used generalized linear model with binomial error structure (GLM) of scores of the post-attack reaction of the skinks and the unpalatability index. The post-attack reaction was ranked as follows: 0 if the mimic was eaten, one if it was eaten but the skink cleaned its mouth, and two if the skink spat out the mimic. The ranks were then scaled to 0–1 range. To compare the relative capture frequencies of Lampona on the prey species a test was used. To test whether overall mortality of prey is related to its unpalatability we used a general linear model (LM) of the sum of all probabilities of capture (pij) by the three predators and unpalatability index. The sum of capture rate was expressed as a killing value, Kj using the formula: Kj=∑j=1n−log(1−pij). All statistical analyses were performed in R (R Core Team, 2013). For each model, diagnostic plots were used to assess model adequacy, such as variance homogeneity and distribution of residuals. Add a comment + Open annotations. The current annotation count on this page is being calculated. Data availability The following data sets were generated (2016) Data from: The golden mimicry complex uses a spectrum of defences to deter a community of predators Available at Repository under a CC0 Dedication. 88. PubMed 566. 66. PubMed 217. [SEP] Justify:

Output: The text is irrelevant to the claim regarding the intelligence of ants, as it discusses a study on mimicry and predation involving skinks and spiders without any mention of ants or their intelligence. It provides detailed methodology for statistical analysis but does not address the claim.