The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index; 7 of the 69 sources its claims cite were checked for support.
Artificial intelligence in education: Addressing ethical challenges in K-12 settings
Open accessAbstract
Artificial intelligence (AI) is a field of study that combines the applications of machine learning, algorithm productions, and natural language processing. Applications of AI transform the tools of education. AI has a variety of educational applications, such as personalized learning platforms to promote students' learning, automated assessment systems to aid teachers, and facial recognition systems to generate insights about learners' behaviors. Despite the potential benefits of AI to support students' learning experiences and teachers' practices, the ethical and societal drawbacks of these systems are rarely fully considered in K-12 educational contexts. The ethical challenges of AI in education must be identified and introduced to teachers and students. To address these issues, this paper (1) briefly defines AI through the concepts of machine learning and algorithms; (2) introduces applications of AI in educational settings and benefits of AI systems to support students' learning processes; (3) describes ethical challenges and dilemmas of using AI in education; and (4) addresses the teaching and understanding of AI by providing recommended instructional resources from two providers-i.e., the Massachusetts Institute of Technology's (MIT) Media Lab and Code.org. The article aims to help practitioners reap the benefits and navigate ethical challenges of integrating AI in K-12 classrooms, while also introducing instructional resources that teachers can use to advance K-12 students' understanding of AI and ethics.
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Claims the paper makes
28 Sources not found · 19 No full text of the sources · 12 Own interpretation · 5 Partly supported · 3 Not checked yet · 3 No basis given · 2 Couldn’t confirm · accuracy not yet measured
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“In essence, algorithms reflect the values of their builders who hold positions of power [26].”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Ethical concerns and potential risks of AI applications in education
What the sources say
- Could not verifycites [26] Hrastinski, S., Olofsson, A.D., Arkenback, C., Ekström, S., Ericsson, E., Fransson, G., Jaldemark, J., Ryberg, T., Öberg, L., Fuentes, A., Gustafsson, U., Humble, N., Mozelius, P., Sundgren, M., Utterberg, M.: Critical imaginaries and reflections on artificial intelligence and robots in postdigital K-12 education. Postdigit. Sci. Educ. 1, 427–445 (2019) sourceExperimental — accuracy not yet measured
The Introduction says business interests might shape the agenda for AI in education, and the Artificial Intelligence in Education section discusses economic interests and demand-driven design. Neither passage says that algorithms reflect their builders’ values or that those builders hold positions of power; searches for those ideas and for “algorithms” found no passage addressing the claim. (LLM, conf=0.72)
plaindirect- Asserted: passes — The authors put it forward as true.
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The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“These systems generate models of learners using their knowledge and cognition; however, the existing platforms do not yet provide models for learners’ social, emotional, and motivational states [28].”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Benefits of AI applications in education > Personalized learning systems
What the sources say
- Partly supportedcites [28] Herder, E., Sosnovsky, S., Dimitrova, V.: Adaptive intelligent learning environments. In: Duval, Erik, Sharples, Mike, Sutherland, Rosamund (eds.) Technology enhanced learning, pp. 109–114. Springer International Publishing, Cham (2017) sourceExperimental — accuracy not yet measured
The paper says cognitive state is a main factor in adaptation (Modeling Knowledge, Cognition and Metacognition, p. 4) and that emotion and affect are not yet part of a typical AILE learner model (Beyond Cognition, p. 5). It does not make the same claim about social or motivational states, and its qualifier is “a typical AILE,” not all existing platforms. (LLM, conf=0.85)
plain- Asserted: passes — The authors put it forward as true.
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- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Existing research on AI in education provides insight on supporting students’ understanding and use of AI [2, 13]”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Future directions for research and teaching on AI and ethics
What the sources say
- Not matchedcites [2] Ali, S. A., Payne, B. H., Williams, R., Park, H. W., Breazeal, C.: Constructionism, ethics, and creativity: developing primary and middle school artificial intelligence education. Paper presented at International Workshop on Education in Artificial Intelligence (EDUAI). Palo Alto, CA, USA. (2019)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Partly supportedcites [13] Borenstein, J., Howard, A.: Emerging challenges in AI and the need for AI ethics education. AI Ethics 1, 61–65 (2020) sourceExperimental — accuracy not yet measured
Section 5 (page unavailable) offers guidance for teaching students the ethical design of AI and helping them understand its ethical challenges. The paper focuses on AI ethics education for future developers, rather than students’ understanding and use of AI more generally. (LLM, conf=0.82)
plainconditional- Asserted: passes — The authors put it forward as true.
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The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Predictive systems which are powered by algorithms jeopardize students and teachers’ autonomy and their ability to govern their own life [46, 47].”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Ethical concerns and potential risks of AI applications in education
What the sources say
- Partly supportedcites [46] Piano, S.L.: Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward. Humanit. Soc. Sci. Commun. 7(9), 1–7 (2020) sourceExperimental — accuracy not yet measured
The Introduction (page unavailable) says that ceding functions to algorithms can diminish human control, and “Points of friction between ethical dimensions” discusses retaining human decision-making autonomy. The paper lists education as an application, but these passages do not establish that predictive systems threaten students’ and teachers’ autonomy or their ability to govern their lives. (LLM, conf=0.86)
- Not checked — no full text of the sourcecites [47] Regan, P.M., Jesse, J.: Ethical challenges of edtech, big data and personalized learning: twenty-first century student sorting and tracking. Ethics Inf. Technol. 21, 167–179 (2019) source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Comparing students’ scores on researcher-developed or standardized tests, research shows that the instruction based on personalized learning systems resulted in higher test scores than traditional teacher-led instruction [36].”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Benefits of AI applications in education > Personalized learning systems
What the sources say
- Not checked — no full text of the sourcecites [36] Ladson-Billings, G.: Toward a theory of culturally relevant pedagogy. Am. Educ. Res. J. 32(3), 465–491 (1995) source
plaindirect- Asserted: passes — The authors put it forward as true.
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A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“On the other hand, AI cannot be a substitute for human interaction [22, 47].”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Benefits of AI applications in education > Social networking sites and chatbots
What the sources say
- Not checked — no full text of the sourcecites [22] Dishon, G.: New data, old tensions: big data, personalized learning, and the challenges of progressive education. Theory Res. Educ. 15(3), 272–289 (2017) source
- Not checked — no full text of the sourcecites [47] Regan, P.M., Jesse, J.: Ethical challenges of edtech, big data and personalized learning: twenty-first century student sorting and tracking. Ethics Inf. Technol. 21, 167–179 (2019) source
plain- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The ethical challenges and risks posed by AI systems seemingly run counter to marketing efforts that present algorithms to the public as if they are objective and value-neutral tools.”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Ethical concerns and potential risks of AI applications in education
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In the paper
“Even though the bias is embedded into the algorithmic model with no explicit intention, we can see various gender and racial biases in different AI-based platforms [54].”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Ethical concerns and potential risks of AI applications in education
What the sources say
- Not checked — no full text of the sourcecites [54] Stahl, B.C., Wright, D.: Ethics and privacy in ai and big data: implementing responsible research and innovation. IEEE Secur. Priv. 16(3), 26–33 (2018) source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Whenever people create algorithms, they also create a set of data that represent society’s historical and systemic biases, which ultimately transform into algorithmic bias.”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Ethical concerns and potential risks of AI applications in education
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Automated assessment systems are becoming one of the most prominent and promising applications of machine learning in K-12 education [42].”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Benefits of AI applications in education > Automated assessment systems
What the sources say
- Not checked — no full text of the sourcecites [42] Murphy, R. F.: Artificial intelligence applications to support k–12 teachers and teaching: a review of promising applications, challenges, and risks. Perspective. 1–20 (2019). https://doi.org/10.7249/PE315 source
plain- Asserted: passes — The authors put it forward as true.
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- It may state two separate claims as one. 28
- Probably not a substantial claim. 22
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“These algorithms do not occur in a vacuum; rather, they shape and are shaped by ever-evolving cultural, social, institutional and political forces and structures [33, 34].”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Introduction
What the sources say
- Not matchedcites [33] Johnson, M.: A scalable approach to reducing gender bias in Google translate. https://ai.googleblog.com/2020/04/a-scalable-approach-to-reducing-gender.html Accessed 26 March 2021
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Supportedcites [34] Ko, A.J., Oleson, A., Ryan, N., Register, Y., Xie, B., Tari, M., Davidson, M., Druga, S., Loksa, D.: It is time for more critical CS education. Commun. ACM 63(11), 31–33 (2020) source
The Abstract says that computing does not occur in a vacuum and both shapes and is shaped by social, cultural, institutional, and political forces. In the same passage, it identifies data and algorithms as part of the systems under discussion, supporting the citing sentence’s paraphrase. (LLM, conf=0.91)
plaindirect- Asserted: passes — The authors put it forward as true.
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The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“More specifically, applications of AI can support instruction in mixed-ability classrooms; while personalized learning systems provide students with detailed and timely feedback about their writing products, automated assessment systems support teachers by freeing them from excessive workloads [26, 42].”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Introduction
What the sources say
- Partly supportedcites [26] Hrastinski, S., Olofsson, A.D., Arkenback, C., Ekström, S., Ericsson, E., Fransson, G., Jaldemark, J., Ryberg, T., Öberg, L., Fuentes, A., Gustafsson, U., Humble, N., Mozelius, P., Sundgren, M., Utterberg, M.: Critical imaginaries and reflections on artificial intelligence and robots in postdigital K-12 education. Postdigit. Sci. Educ. 1, 427–445 (2019) sourceExperimental — accuracy not yet measured
The Individualization section reports expectations that AI could help teachers individualize classroom teaching, and the Role of the Teacher section describes possible assistance with assessment and less work. These are prospective views, not demonstrated effects. Neither section supports the specific claim that personalized systems give detailed, timely feedback on students’ writing. (LLM, conf=0.87)
- Not checked — no full text of the sourcecites [42] Murphy, R. F.: Artificial intelligence applications to support k–12 teachers and teaching: a review of promising applications, challenges, and risks. Perspective. 1–20 (2019). https://doi.org/10.7249/PE315 source
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
- Partly supported
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“These AI systems can increase the capacity of K-12 educational systems and support the social and cognitive development of students and teachers [55, 8].”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Introduction
What the sources say
- Not matchedcites [55] The Institute for Ethical AI in Education. Interim report: towards a shared vision of ethical AI in education. https://www.buckingham.ac.uk/wp-content/uploads/2020/02/The-Institute-for-Ethical-AI-in-Educations-Interim-Report-Towards-a-Shared-Vision-of-Ethical-AI-in-Education.pdf. (2020)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Could not verifycites [8] Greenhow, C., Galvin, S., Staudt Willet, K.B.: Inquiring tweets want to know: #Edchat supports for #remoteteaching during COVID-19. British Journal of Educational Technology. 1–21, (2021) sourceExperimental — accuracy not yet measured
The Introduction discusses teachers’ professional learning through social media, and the Discussion describes #Edchat as a source of just-in-time support for remote teaching (page numbers unavailable). Searches for AI systems, artificial intelligence, K–12 educational capacity, and social and cognitive development found no passage addressing the claimed effects of AI systems. (LLM, conf=0.76)
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“The biggest risks of integrating these algorithms in K-12 contexts are: (a) perpetuating existing systemic bias and discrimination, (b) perpetuating unfairness for students from mostly disadvantaged and marginalized groups, and (c) amplifying racism, sexism, xenophobia, and other forms of injustice and inequity [40].”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Introduction
What the sources say
- Not matchedcites [40] Miller, F.A., Katz, J.H., Gans, R.: The OD imperative to add inclusion to the algorithms of artificial intelligence. OD Practitioner. 5(1), 6–12 (2018)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“For instance, a text-based chatbot system called “Pounce” was used at Georgia State University to help students through the registration and admission process, as well as financial aid and other administrative tasks [7].”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Benefits of AI applications in education > Social networking sites and chatbots
What the sources say
- Not checked — no full text of the sourcecites [7] Greenhow, C., Galvin, S., Brandon, D., Askari, E.: A decade of research on K-12 teaching and teacher learning with social media: insights on the state-of-the-field. Teachers College Record 122(6), 1–7 (2020) source
plainSingle case- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Considering the shift to remote K-12 education during the COVID-19 pandemic, personalized learning systems offer a promising form of distance learning that could reshape K-12 instruction for the future [35].”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Benefits of AI applications in education > Personalized learning systems
What the sources say
- Not matchedcites [35] Krueger, N.: Artificial intelligence has infiltrated our lives. Can it improve learning? International Society for Technology in Education (ISTE). (2017).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedgedconditional- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In summary, applications of AI can positively impact students’ and teachers’ educational experiences and help them address instructional challenges and concerns.”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Benefits of AI applications in education > Social networking sites and chatbots
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“These systems provide insights about students’ behaviors during learning processes and allow teachers to take action or intervene, which, in turn, helps teachers develop learner-centered practices and increase student’s engagement [55].”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Benefits of AI applications in education > Facial recognition systems and predictive analytics
What the sources say
- Not matchedcites [55] The Institute for Ethical AI in Education. Interim report: towards a shared vision of ethical AI in education. https://www.buckingham.ac.uk/wp-content/uploads/2020/02/The-Institute-for-Ethical-AI-in-Educations-Interim-Report-Towards-a-Shared-Vision-of-Ethical-AI-in-Education.pdf. (2020)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, these scoring systems have the potential to deal with the complexities of the teaching context and support students’ learning process by providing them with feedback and guidance to improve and revise their writing.”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Benefits of AI applications in education > Automated assessment systems
hedgedconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“On the other hand, a tool called “Gradescope” has been used by over 500 universities to develop and streamline scoring and assessment [12].”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Benefits of AI applications in education > Automated assessment systems
What the sources say
- Not matchedcites [12] Blumenstyk, G.: Can artificial intelligence make teaching more personal? The Chronicle of Higher Education. https://www.chronicle.com/article/Can-Artificial-Intelligence/243023 (2018)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Researchers have emphasized the importance of using SNSs (such as Facebook) to expand learning opportunities beyond the classroom, monitor students’ well-being, and deepen student–teacher relations [5].”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Benefits of AI applications in education > Social networking sites and chatbots
What the sources say
- Not checked — no full text of the sourcecites [5] Asterhan, C.S.C., Rosenberg, H.: The promise, reality and dilemmas of secondary school teacher–student interactions in Facebook: the teacher perspective. Comput. Educ. 85, 134–148 (2015) source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“They point out that the integration of social media can foster students’ active learning, collaboration skills, and connections with communities beyond the classroom [6].”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Benefits of AI applications in education > Social networking sites and chatbots
What the sources say
- Not checked — no full text of the sourcecites [6] Krutka, D., Manca, S., Galvin, S., Greenhow, C., Koehler, M., Askari, E.: Teaching “against” social media: confronting problems of profit in the curriculum. Teachers College Record 121(14), 1–42 (2019) source
plainconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“Social media are increasingly integrated into K-12 education [7] and subordinate learners’ activities to intelligent algorithm systems [17].”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Introduction
What the sources say
- Not checked — no full text of the sourcecites [7] Greenhow, C., Galvin, S., Brandon, D., Askari, E.: A decade of research on K-12 teaching and teacher learning with social media: insights on the state-of-the-field. Teachers College Record 122(6), 1–7 (2020) source
- Not checked — no full text of the sourcecites [17] Cheney-Lippold, J.: We are data: algorithms and the making of our digital selves. New York University Press, New York (2017) source
plaindirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although AI can be a time-saving and cognitive aide for teachers, it is but one tool in the teachers’ toolkit.”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Benefits of AI applications in education > Social networking sites and chatbots
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: Students have a wide range of learning styles and needs.
In the paper
“For instance, AI has a variety of algorithmic applications in education, such as personalized learning systems to promote students’ learning, automated assessment systems to support teachers in evaluating what students know, and facial recognition systems to provide insights about learners’ behaviors [49].”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Introduction
What the sources say
- Not matchedcites [49] Remian, D.: Augmenting education: ethical considerations for incorporating artificial intelligence in education (Unpublished master’s thesis). University of Massachusetts, Boston (2019)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“To create a future generation where an inclusive and diverse citizenry can participate in the development of the future of AI, we need to develop opportunities for K-12 students and teachers to learn about AI via AI- and ethics-based curricula and professional development [2, 58]”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Introduction
What the sources say
- Not matchedcites [2] Ali, S. A., Payne, B. H., Williams, R., Park, H. W., Breazeal, C.: Constructionism, ethics, and creativity: developing primary and middle school artificial intelligence education. Paper presented at International Workshop on Education in Artificial Intelligence (EDUAI). Palo Alto, CA, USA. (2019)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites [58] Zimmerman, M.: Teaching AI: exploring new frontiers for learning. International Society for Technology in Education, Portland (2018)
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence.
- Rests on an assumption: The ethical and societal drawbacks of AI systems are rarely fully considered in K-12 educational contexts.
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In the paper
“Most studies reflect university students’ engagement with ethical ideas about algorithmic bias, but few addresses how to promote students’ understanding of AI and ethics in K-12 settings.”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Introduction
What the sources say
- Not matchedcites [2] Ali, S. A., Payne, B. H., Williams, R., Park, H. W., Breazeal, C.: Constructionism, ethics, and creativity: developing primary and middle school artificial intelligence education. Paper presented at International Workshop on Education in Artificial Intelligence (EDUAI). Palo Alto, CA, USA. (2019)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Toward this end, the existing literature provides little guidance and contains a limited number of studies that focus on supporting K-12 students and teachers’ understanding of social, cultural, and ethical implications of AI [2].”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Introduction
What the sources say
- Not matchedcites [2] Ali, S. A., Payne, B. H., Williams, R., Park, H. W., Breazeal, C.: Constructionism, ethics, and creativity: developing primary and middle school artificial intelligence education. Paper presented at International Workshop on Education in Artificial Intelligence (EDUAI). Palo Alto, CA, USA. (2019)
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In the paper
“For example, these analytics can be used to detect university students who are at risk of failing or not completing a course. Through these identifications, instructors can intervene and get students the help they need [55].”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Benefits of AI applications in education > Facial recognition systems and predictive analytics
What the sources say
- Not matchedcites [55] The Institute for Ethical AI in Education. Interim report: towards a shared vision of ethical AI in education. https://www.buckingham.ac.uk/wp-content/uploads/2020/02/The-Institute-for-Ethical-AI-in-Educations-Interim-Report-Towards-a-Shared-Vision-of-Ethical-AI-in-Education.pdf. (2020)
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In the paper
“AI dominates the fields of science, engineering, and technology, but also is present in education through machine-learning systems and algorithm productions [43].”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Introduction
What the sources say
- Not checked — no full text of the sourcecites [43] Naqvi, A.: Artificial intelligence for audit, forensic accounting, and valuation: a strategic perspective. Wiley (2020) source
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In the paper
“Students train a cat–dog classifier using two different datasets. While the first dataset reflects the cats as the over-represented group, the second dataset indicates the equal and diverse representation between dogs and cats [2].”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Teaching and understanding AI and ethics in educational settings > "AI and Ethics Curriulum" for middle school students by MIT Media Lab
What the sources say
- Not matchedcites [2] Ali, S. A., Payne, B. H., Williams, R., Park, H. W., Breazeal, C.: Constructionism, ethics, and creativity: developing primary and middle school artificial intelligence education. Paper presented at International Workshop on Education in Artificial Intelligence (EDUAI). Palo Alto, CA, USA. (2019)
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In the paper
“while Google Translate translated the Turkish equivalent of “She/he is a nurse” into the feminine form, it also translated the Turkish equivalent of “She/he is a doctor” into the masculine form [33].”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Ethical concerns and potential risks of AI applications in education
What the sources say
- Not matchedcites [33] Johnson, M.: A scalable approach to reducing gender bias in Google translate. https://ai.googleblog.com/2020/04/a-scalable-approach-to-reducing-gender.html Accessed 26 March 2021
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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In the paper
“The second workshop in the series is “Designing ads with transparency: A creative workshop”. Through this workshop, students are able to think further about the meaning, aim, and impact of advertising and the role of advertisements in our lives [44].”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Teaching and understanding AI and ethics in educational settings > “AI and Data Privacy” workshop series for K-9 students by MIT Media Lab
What the sources say
- Not matchedcites [44] Nguyen, S., DiPaola, D.: AI & data privacy activities for k-9 students. MIT Media Lab. https://www.media.mit.edu/projects/data-privacy-design-for-youth/overview/ (2020)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“England’s A-level and GCSE secondary level examinations were cancelled due to the pandemic in the summer of 2020 [1, 57].”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Ethical concerns and potential risks of AI applications in education
What the sources say
- Not matchedcites [1] Adams, R., McIntyre, N.: England A-level downgrades hit pupils from disadvantaged areas hardest.https://www.theguardian.com/education/2020/aug/13/england-a-level-downgrades-hit-pupils-from-disadvantaged-areas-hardest (2020). Accessed 10 September 2020
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [57] Weale, S., Stewart, H.: A-level and GCSE results in England to be based on teacher assessments in U-turn. The Guardian, (2020).
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In the paper
“Through a series of lesson plans and hand-on activities, teachers are guided to support students’ learning of the technical terminology of AI systems as well as the ethical and societal implications of AI [2].”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Teaching and understanding AI and ethics in educational settings > "AI and Ethics Curriulum" for middle school students by MIT Media Lab
What the sources say
- Not matchedcites [2] Ali, S. A., Payne, B. H., Williams, R., Park, H. W., Breazeal, C.: Constructionism, ethics, and creativity: developing primary and middle school artificial intelligence education. Paper presented at International Workshop on Education in Artificial Intelligence (EDUAI). Palo Alto, CA, USA. (2019)
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“it revealed how the score distribution favored students who attended private or independent schools, while students from underrepresented groups were hit hardest.”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Ethical concerns and potential risks of AI applications in education
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“people’s agency diminishes as AI systems reduce introspective and independent thought [55].”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Ethical concerns and potential risks of AI applications in education
What the sources say
- Not matchedcites [55] The Institute for Ethical AI in Education. Interim report: towards a shared vision of ethical AI in education. https://www.buckingham.ac.uk/wp-content/uploads/2020/02/The-Institute-for-Ethical-AI-in-Educations-Interim-Report-Towards-a-Shared-Vision-of-Ethical-AI-in-Education.pdf. (2020)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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In the paper
“One of the main learning goals is to introduce students to basic components of AI through algorithms, datasets, and supervised machine-learning systems all while underlining the problem of algorithmic bias [45].”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Teaching and understanding AI and ethics in educational settings > "AI and Ethics Curriulum" for middle school students by MIT Media Lab
What the sources say
- Not matchedcites [45] Payne, B. H.: An ethics of artificial intelligence curriculum for middle school students. MIT Media Lab. https://www.media.mit.edu/projects/ai-ethics-for-middle-school/overview/ (2019)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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In the paper
“In this way, students are able to figure out that algorithms can have various motives and goals.”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Teaching and understanding AI and ethics in educational settings > "AI and Ethics Curriulum" for middle school students by MIT Media Lab
What the sources say
- Not matchedcites [45] Payne, B. H.: An ethics of artificial intelligence curriculum for middle school students. MIT Media Lab. https://www.media.mit.edu/projects/ai-ethics-for-middle-school/overview/ (2019)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“students identify different stakeholders (such as their parents, teacher, or doctor) who care about their peanut butter and jelly sandwich algorithm. In this way, the values and opinions of those stakeholders also are embedded in the algorithm.”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Teaching and understanding AI and ethics in educational settings > "AI and Ethics Curriulum" for middle school students by MIT Media Lab
What the sources say
- Not matchedcites [45] Payne, B. H.: An ethics of artificial intelligence curriculum for middle school students. MIT Media Lab. https://www.media.mit.edu/projects/ai-ethics-for-middle-school/overview/ (2019)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Monitoring and tracking students’ online conversations and actions also may limit their participation in the learning event and make them feel unsafe to take ownership for their ideas.”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Ethical concerns and potential risks of AI applications in education
hedgedconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“Through algorithms and machine-learning models, AI tracking systems not only necessitate monitoring of activities but also determine the future preferences and actions of their users [47].”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Ethical concerns and potential risks of AI applications in education
What the sources say
- Not checked — no full text of the sourcecites [47] Regan, P.M., Jesse, J.: Ethical challenges of edtech, big data and personalized learning: twenty-first century student sorting and tracking. Ethics Inf. Technol. 21, 167–179 (2019) source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Although existing legislation and standards exist to protect sensitive personal data, AI-based tech companies’ violations with respect to data access and security increase people’s privacy concerns [42, 54].”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Ethical concerns and potential risks of AI applications in education
What the sources say
- Not checked — no full text of the sourcecites [42] Murphy, R. F.: Artificial intelligence applications to support k–12 teachers and teaching: a review of promising applications, challenges, and risks. Perspective. 1–20 (2019). https://doi.org/10.7249/PE315 source
- Not checked — no full text of the sourcecites [54] Stahl, B.C., Wright, D.: Ethics and privacy in ai and big data: implementing responsible research and innovation. IEEE Secur. Priv. 16(3), 26–33 (2018) source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Code.org, a nonprofit organization focused on expanding students’ participation in computer science. Sponsored by Microsoft, Facebook, Amazon, Google, and other tech companies, Code.org aims to provide opportunities for K-12 students to learn about AI and machine-learning systems [20].”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Teaching and understanding AI and ethics in educational settings > Investigation of “AI for Oceans” by Code.org
What the sources say
- Not matchedcites [20] Code.org.: AI for Oceans. https://code.org/oceans (2020)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“In their AI Bingo chart, students try to identify what prediction the selected AI system makes and what dataset it uses.”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Teaching and understanding AI and ethics in educational settings > "AI and Ethics Curriulum" for middle school students by MIT Media Lab
What the sources say
- Not matchedcites [45] Payne, B. H.: An ethics of artificial intelligence curriculum for middle school students. MIT Media Lab. https://www.media.mit.edu/projects/ai-ethics-for-middle-school/overview/ (2019)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“The final workshop in MIT’s AI and data privacy series is “Designing Privacy in Social Media Platforms”. This workshop is designed to teach students about YouTube, design, civics, and data privacy [44].”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Teaching and understanding AI and ethics in educational settings > “AI and Data Privacy” workshop series for K-9 students by MIT Media Lab
What the sources say
- Not matchedcites [44] Nguyen, S., DiPaola, D.: AI & data privacy activities for k-9 students. MIT Media Lab. https://www.media.mit.edu/projects/data-privacy-design-for-youth/overview/ (2020)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
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In the paper
“The first workshop in the series is “Mystery YouTube Viewer: A lesson on Data Privacy”. During the workshop, students engage with the question of what privacy and data mean [44]. They observe YouTube’s home page from the perspective of a mystery user.”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Teaching and understanding AI and ethics in educational settings > “AI and Data Privacy” workshop series for K-9 students by MIT Media Lab
What the sources say
- Not matchedcites [44] Nguyen, S., DiPaola, D.: AI & data privacy activities for k-9 students. MIT Media Lab. https://www.media.mit.edu/projects/data-privacy-design-for-youth/overview/ (2020)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Different scholars have examined the role of social media in education, describing its impact on student and teacher learning and scholarly communication [6].”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Benefits of AI applications in education > Social networking sites and chatbots
What the sources say
- Not checked — no full text of the sourcecites [6] Krutka, D., Manca, S., Galvin, S., Greenhow, C., Koehler, M., Askari, E.: Teaching “against” social media: confronting problems of profit in the curriculum. Teachers College Record 121(14), 1–42 (2019) source
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“The activity is provided as an open-access tutorial for teachers to help their students explore how to train, model and classify data, as well as to understand how human bias plays a role in machine-learning systems. During the activity, students first classify the objects as either “fish” or “not fish” in an attempt to remove trash from the ocean.”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Teaching and understanding AI and ethics in educational settings > Investigation of “AI for Oceans” by Code.org
What the sources say
- Not matchedcites [20] Code.org.: AI for Oceans. https://code.org/oceans (2020)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
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In the paper
“Another quality resource from the MIT Media Lab’s Personal Robots Group is a workshop series designed to teach students (between the ages 7 and 14) about data privacy and introduce them to designing and prototyping data privacy features.”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Teaching and understanding AI and ethics in educational settings > “AI and Data Privacy” workshop series for K-9 students by MIT Media Lab
What the sources say
- Not matchedcites [44] Nguyen, S., DiPaola, D.: AI & data privacy activities for k-9 students. MIT Media Lab. https://www.media.mit.edu/projects/data-privacy-design-for-youth/overview/ (2020)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
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The paper relies on this without support.
The ethical and societal drawbacks of AI systems are rarely fully considered in K-12 educational contexts.
“Despite the potential benefits of AI to support students’ learning experiences and teachers’ practices, the ethical and societal drawbacks of these systems are rarely fully considered in K-12 educational contexts.”
The paper relies on this without support.
Students have a wide range of learning styles and needs.
“Students have a wide range of learning styles and needs.”
The paper relies on this without support.
Educators will have different ethical concerns and challenges depending on their students' grade and age of development.
“However, it is important to acknowledge that educators will have different ethical concerns and challenges depending on their students’ grade and age of development.”
The paper relies on this without support.
AI monitoring tools collect extensive information about what students and teachers do and prefer.
“Another ethical concern surrounding the use of AI in K-12 education is surveillance or tracking systems which gather detailed information about the actions and preferences of students and teachers.”
The paper relies on this without support.
When public schools mandate AI systems, students and parents effectively cannot opt out, even if they consent to losing privacy.
“They really have no choice if these systems are required by public schools.”
The paper relies on this without support.
Platforms like YouTube serve commonly as instructional tools in K-12 classes and in students' lives outside school.
“social media such as YouTube are widely used as a teaching and learning tool within K-12 classrooms and beyond them, in students’ everyday lives.”
The paper relies on this without support.
Scholarship on preparing K-12 teachers to teach AI and ethics remains at an early stage.
“research on how to develop K-12 teachers’ instructional practices regarding AI and ethics is still in its infancy.”
The paper relies on this without support.
Most privacy breaches happen when individuals reveal too much personal data on online platforms.
“Privacy violations mainly occur as people expose an excessive amount of personal information in online platforms.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“AI systems often require access to vast amounts of student data, including academic performance, behavioral patterns, and even personal information (Akgun and Greenhow, 2022; Kamalov et al., 2023).”
From Exploring the effects of artificial intelligence on student and academic well-being in higher education: a mini-review (2025) · cites this paper as Akgun and Greenhow, 2022
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Educational accessibility refers to GenAI's ability to provide learning opportunities to students with limited resources, allowing equitable access to personalized content (Akgun and Greenhow, 2022).”
From Ethical and regulatory challenges of Generative AI in education: a systematic review (2025) · cites this paper as Akgun and Greenhow, 2022
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The personalization of learning is manifested through GenAI's ability to adapt educational content and methods to the specific needs of each student (Akgun and Greenhow, 2022; Alali and Wardat, 2024).”
From Ethical and regulatory challenges of Generative AI in education: a systematic review (2025) · cites this paper as Akgun and Greenhow, 2022
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Furthermore, the growing use of AI in higher education raises concerns about data privacy and security.”
From Exploring the effects of artificial intelligence on student and academic well-being in higher education: a mini-review (2025) · cites this paper as Akgun and Greenhow, 2022
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“These concerns are also central to current discussions on trustworthy artificial intelligence in education, which highlight the importance of ethical awareness, responsible data use, and critical engagement with AI-driven systems in educational contexts (Akgun & Greenhow, 2022; Luckin et al., 2022).”
From Integrating artificial intelligence into preservice teacher education: enhancing AI literacy and digital material design competency (2026) · cites this paper as Akgun & Greenhow, 2022
The cited source says what the claim attributes to it.
In the citing paper
“AI-related technologies are frequently associated in the literature with personalization, formative assessment, learning analytics, and instructional support (Akgun & Greenhow, 2022;Hrastinski et al., 2019;Murphy, 2019).”
From Teachers' Experiences with AI in K–12 Education: Opportunities and Challenges (2026) · cites this paper as Akgun & Greenhow, 2022
The cited source says what the claim attributes to it.
In the citing paper
“These reflections echo international discussions on responsible and humancentered AI in education, particularly in contexts involving children and vulnerable learners (Akgun & Greenhow, 2022;Jobin et al., 2019).”
From Teachers' Experiences with AI in K–12 Education: Opportunities and Challenges (2026) · cites this paper as Akgun & Greenhow, 2022
The cited source says what the claim attributes to it.
In the citing paper
“Participants raised concerns regarding data privacy, algorithmic bias, fairness, and inequality, situating AI within broader debates on responsible and human-centered technology use (Akgun & Greenhow, 2022;Jobin et al., 2019;Remian, 2019).”
From Teachers' Experiences with AI in K–12 Education: Opportunities and Challenges (2026) · cites this paper as Akgun & Greenhow, 2022
In the citing paper
“Challenges such as AI-based misunderstanding, misleadingness, limitations, and hidden ethical issues behind different platforms have been identified (Akgun & Greenhow, 2021;Sijing & Lan, 2018).”
From Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world (2023) · cites this paper as Akgun & Greenhow, 2021
In the citing paper
“The design of AIED applications could face challenges such as AI-based misunderstanding, misleadingness, limitations, the weak connection to theoretical pedagogical perspectives, and the need for deep exploration of ethical and educational approaches in the application of AIED in education (Akgun & Greenhow, 2021;Zawacki-Richter et al., 2019).”
From A systematic review of literature reviews on artificial intelligence in education (AIED): a roadmap to a future research agenda (2024) · cites this paper as Akgun & Greenhow, 2021
In the citing paper
“GAI systems typically require access to large volumes of student data, including academic records and even personal information (Akgun & Greenhow, 2022).”
From Exploring How AI Literacy and Self-Regulated Learning Relate to Student Writing Performance and Well-Being in Generative AI-Supported Higher Education (2025) · cites this paper as Akgun & Greenhow, 2022
In the citing paper
“Educational uses of generative AI pose several other challenges. One is the perpetuation of biases and discrimination, potentially reinforcing racial or gender-based stereotypes during personalized learning, automated scoring, and admission processes (88, 90).”
From The impact of generative artificial intelligence on socioeconomic inequalities and policy making (2024) · cites this paper as [88]
In the citing paper
“A review of AI applications in education identified several use cases that produced higher test scores when students used personalized learning systems (88).”
From The impact of generative artificial intelligence on socioeconomic inequalities and policy making (2024) · cites this paper as [88]
In the citing paper
“In addition, AI products for teaching, often sourced from third parties and guided by cost considerations, may pose unique challenges such as the risk of bias or rapid obsolescence [66,67].”
From Proposing a Principle-Based Approach for Teaching AI Ethics in Medical Education (2024) · cites this paper as [66]
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Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Teaching and understanding AI and ethics in educational settings
“Code.org’s “AI and Oceans” activity.”
§ Artificial intelligence in education: Addressing ethical challenges in K-12 settings > Teaching and understanding AI and ethics in educational settings > Investigation of “AI for Oceans” by Code.org
“the organization offers an activity called “AI for Oceans””
Citations
Cites
Toward a Theory of Culturally Relevant Pedagogy
Ladson-Billings, G. (1995) · American Educational Research Journal · doi:10.3102/00028312032003465 · Cited by 8,057
Culturally responsive teaching: theory, research, and practice
(2011) · Choice Reviews Online · doi:10.5860/choice.48-2812 · Cited by 3,113
The Handbook of Artificial Intelligence
Salveter, S. C. (1982) · Elsevier eBooks · doi:10.1016/c2013-0-07691-8 · Cited by 1,703
Evolution and Revolution in Artificial Intelligence in Education
Roll, I., & Wylie, R. (2016) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-016-0110-3 · Cited by 1,229
Foundations of Machine Learning
(2012) · The MIT Press eBooks · doi:10.7551/mitpress/8291.003.0006 · Cited by 1,151
Foundations of Machine Learning
Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2018) · doi:10.7551/mitpress/10290.001.0001 · Cited by 1,122
(2019) · Open Research Online (The Open University) · Cited by 1,004
Chatbots for learning: A review of educational chatbots for the Facebook Messenger
Smutny, P., & Schreiberova, P. (2020) · Computers & Education · doi:10.1016/j.compedu.2020.103862 · Cited by 770
Artificial Intelligence and the ‘Good Society’: the US, EU, and UK approach
Cath, C., Wachter, S., Mittelstadt, B., Taddeo, M., & Floridi, L. (2017) · Science and Engineering Ethics · doi:10.1007/s11948-017-9901-7 · Cited by 733 · cited 1×
“For example, the European Union (EU) has highlighted ethical concerns and implications of AI, emphasized privacy protection, surveillance, and nondiscrimination as primary areas of interest, and provided guidelines on how trustworthy AI should be [3, 15, 23].”
BackgroundEmerging challenges in AI and the need for AI ethics education
Borenstein, J., & Howard, A. (2021) · AI and Ethics · doi:10.1007/s43681-020-00002-7 · Cited by 626
Research works printed but not linked: 4
- Ali, S. A., Payne, B. H., Williams, R., Park, H. W., Breazeal, C.: Constructionism, ethics, and creativity: developing primary and middle school artificial intelligence education. Paper presented at International Workshop on Education in Artificial Intelligence (EDUAI). Palo Alto, CA, USA. (2019)
- Citron, D.K., Pasquale, F.A.: The scored society: due process for automated predictions. Wash. Law Rev. 89, 1–33 (2014)
- IEEE Corporate Advisory Group (CAG): IEEE guide for terms and concepts in intelligent process automation. The Institute of Electrical and Electronics Engineers Standards Association. 1–16 (2017). https://ieeexplore.ieee.org/iel7/8070669/8070670/08070671.pdf
- Miller, F.A., Katz, J.H., Gans, R.: The OD imperative to add inclusion to the algorithms of artificial intelligence. OD Practitioner. 5(1), 6–12 (2018)
Cited by
Sorted by citations
Items 1,201–1,300 of 1,448
Sam, R., Kheuy, S., Heak, H., Hoeurn, S., Phorn, P., Kheav, S., Em, S., No, S., Keo, V., & Lan, B. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/407418
Empowering Education 4.0 Using Artificial Intelligence
Paul, P., Bommali, T., Kayyali, M., et al. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/407420
Ak, N. Y., & Altın, R. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-5865-9.ch008
Artificial Intelligence in Nigeria and Kazakhstan Educational Systems
Adetoba, O. M., Ibrahim, H. K., & Ebiniyi, O. E. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/407454
Generative AI in Education: Exploring Ethical Challenges and Solutions
Kamruzzaman, A., Samdrup, N., & Thakur, K. (2026) · International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) · doi:10.1109/acdsa67686.2026.11468251
“Similar issues appear in Kenyan schools, where weak data protection laws put students at risk when AI is used [7].”
Artificial Intelligence Ethics in Digital Citizenship Education
Sırvermez, U., Baltaci-Goktalay, S., & ONAN, B. C. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-6566-4.ch008 · Cited by 2
Theoretical Frameworks for Implementing Game-Based Learning in Language Development and Acquisition
Ghosh, O., & Kumar, B. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-4322-8.ch010
Harnessing AI in Blended Learning for Language Skill Development
Ait-Hroch, A., Gazi, S. E., & Ibrahimi, A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/407556
Artificial Intelligence in Academia: Best Practices for Armenian State University of Economics
HARUTYUNYAN, L., Bekaryan, L., Hakobyan, A. G., et al. (2026) · IT Professional · doi:10.1109/mitp.2026.3665817
A Traceability Matrix Model for Quantitative Ethical Evaluation of AI Learning Algorithms
Saranya, S. P., Abinaya, S., & Srianushya, R. (2026) · International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) · doi:10.1109/icses66558.2026.11478901
Iturralde, D. D. O. (2026) · Etic net Revista científica electrónica de Educación y Comunicación en la Sociedad del Conocimiento · doi:10.30827/eticanet.v26i1.31168 · Cited by 2
From Digital Assistants to Cognitive Crutches
Irfan, M. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7097-2.ch006
The Professor's Role in an AI-Dominated Classroom in Higher Education
Kayyali, M. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7097-2.ch011
Imelda, C., ROHMAH, S., & Furoidah, A. (2025) · Khazanah Journal of Islamic Education and Science · doi:10.61815/khazanah.v1i1.865
GENERATIVNÍ UMĚLÁ INTELIGENCE VE VÝUCE BIOLOGIE: TVORBA FOTOREALISTICKÝCH SNÍMKŮ HMYZU
Hrušková, P., Hruška, A., & Mourek, J. (2026) · Biologie Chemie Zeměpis · doi:10.14712/25337556.2025.4.1
Kurniati, I., Perdana, M. T., Sutomo, H. I., & Laxmi (2025) · Khazanah Journal of Islamic Education and Science · doi:10.61815/khazanah.v1i1.862
Sadigova, S. I., Aliyeva, D. K. R., Ahmadova, V. S., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-9514620/v1
Teacher Alienation in the AI Era
Küçükuncular, A. (2026) · Artificial intelligence · doi:10.5772/intechopen.1014101 · Cited by 1
Modeling the dynamic interplay between student creativity and Gen-AI
Idrisi, M. J., Ullah, M. S., Shamsi, N. A., Thakur, M., & Beza, D. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01284-2
“More recent frameworks emphasize adaptive learning and learner agency [33], while ethical debates around transparency, fairness, and explainability continue to grow [1, 29, 31].”
Far, S. B., Qazani, M. R. C., & Rad, A. I. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11567-w
Sharma, E., & Gupta, A. (2026) · Teacher Education and Curriculum Studies · doi:10.11648/j.tecs.20261102.13
The Ethical Dimension: General Principles and Educational Perspectives
Ranieri, M. (2026) · Digital education and learning · doi:10.1007/978-3-032-15919-9_5
Maintaining Ethics in ODeL Teaching and Learning in Higher Education With the Rise of AI
Nxumalo, M. M., & Ndlovu, S. (2026) · IGI Global eBooks · doi:10.4018/979-8-2600-1443-1.ch010
Bezzina, S., & Dingli, A. (2026) · Studies in computational intelligence · doi:10.1007/978-3-032-16153-6_6
Jayalath, N., & Rajapakse, C. (2026) · International Research Conference on Smart Computing and Systems Engineering (SCSE) · doi:10.1109/scse70081.2026.11499921
“…importance of topics like data privacy, AI overuse, and academic integrity – areas that warrant greater emphasis in curricula and future instruments. for example, students worried about data sharing and algorithmic discrimination, issues highlighted as major ethical challenges in K–12 contexts[29].”
Almagribi, A. B., Putranto, B. P. D., Redjeki, S., Isnan, M., Gusteti, M. U., & Erfan, M. (2025) · International Conference on Decision Aid Sciences and Applications (DASA) · doi:10.1109/dasa68193.2025.11499128
“Despite their potential, these technologies pose risks including privacy violations [3], user dissatisfaction with outputs, and the potential for over-reliance, which may hinder the development of critical thinking skills [4].”
Agsimon, T. (2026) · Research Square · doi:10.21203/rs.3.rs-9614045/v1
Beyond Decoloniality: Embracing a Third-space of Interculturality in Higher Education
Xu, Y. (2026) · Emerald Group Publishing Limited eBooks · doi:10.1108/s1479-367920260000052027
SS-GAN: A Text-to-Face Generation Method for Education Applications
Wang, H., Wang, Y., & Zhao, G. (2026) · Communications in computer and information science · doi:10.1007/978-981-95-7254-0_12
Mokhtar, K. A. E., Zerhane, R., Aidoun, A., & Janati‐Idrissi, R. (2026) · Multidisciplinary Science Journal · doi:10.31893/multiscience.2026624
Policy and Governance for AI-Driven Educational Transformation
Saeed, F., & Abdelaziz, H. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-6063-8.ch008
Navigating the Shifts in AI-Augmented Classrooms
Sarkar, R., & Chakrabarty, N. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-6063-8.ch007
Papanastasiou, E. C., Pittas, E., Rodosthenous-Balafa, M., & Λαμπρόπουλος, Γ. (2026) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2026.100603 · Cited by 1
Pre-service Teachers’ Experiences in Instructional Design with Artificial Intelligence Tools
Ayaz, E., & Ekinci, V. (2026) · Springer eBooks · doi:10.1007/978-3-032-18030-8_8
Fathi, T. E., Lamri, D., & Ibrahmi, E. M. A. (2026) · Springer eBooks · doi:10.1007/978-3-032-18030-8_10
Examining Artificial Intelligence Curricula for Ethical Principles—A Bridge to Human Flourishing
Mak, J., Wen, W., & Castellana, M. A. (2026) · Springer eBooks · doi:10.1007/978-3-032-17190-0_3
Wang, R., Sun, X., Sun, Y., & Wang, J. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1794568 · Cited by 3
“…AI and education shows that such problems are not simply technical errors but are shaped by broader social and institutional conditions, which means that fairness, accountability, and transparency must be treated as design and governance issues as well as modeling issues (Akgun and Greenhow, 2022).”
A systematic review of human-LLM interactions in computational thinking empirical studies
Zhang, Y., Song, Y., Precup, D., Rabbany, R., & Cutumisu, M. (2026) · Computer Science Education · doi:10.1080/08993408.2026.2665177
Hu, L., & Nasir, M. K. M. (2026) · Sustainability · doi:10.3390/su18104766
Giacosa, A., & Triki, N. (2026) · Springer eBooks · doi:10.1007/978-3-032-16520-6_7
Manojkumar, R., Gundgurti, P. E., Srinivas, T. A. S., Pittala, R. B., Konda, A. S., & Bairwa, B. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-1840-8.ch005
Privacy Concerns of AI-Based Surveillance Systems Under FERPA and Educational Privacy Laws
Hackman, S., Johnson, M., Taylor, W. C., Yellamraju, A., & Taeb, M. (2026) · ACM Southeast Regional Conference (ACMSE) · doi:10.1145/3746467.3801527
Chen, Y. (2025) · International Conference on Information Education and Artificial Intelligence (ICIEAI) · doi:10.1145/3799457.3799582
“In STEM education research, clustering analysis has successfully revealed diverse learner archetypes, each responding differently to instructional strategies and demonstrating unique combinations of attitudes, self-efficacy, and career intentions [9, 10].”
예비교사의 AI 윤리 인식 차이 분석: 전공-성별-연령-AI 활용 경험을 중심으로
Maeng, U., & Ko, H. K. (2026) · Modern English Education · doi:10.18095/meeso.2026.27.1.160 · Cited by 1
Upadhayaya, K. K. (2026) · Advances in Mobile Learning Educational Research · doi:10.25082/amler.2026.02.001
Developing an Integrated AI Pedagogical Framework for Pre-Service Teachers
Dua, B., & Gupta, A. (2026) · Review of Artificial Intelligence in Education · doi:10.37497/rev.artif.intell.educ.v7ii.91
Quality Science Education: Role of Digital Technologies in the Developing World
Misra, R., Rahul, Nishu, & Mathur, V. (2026) · Springer eBooks · doi:10.1007/978-981-95-7785-9_9
Stracke, C. M. (2026) · AI and Ethics · doi:10.1007/s43681-026-01092-5 · Cited by 1
“The identifica - tion of the necessity of ethical AI&ED and the development of community-driven proposals and frameworks for ethical AI&ED took twenty years [2 , 10, 25 , 56, 57 ].”
Lina, L., Kumari, M., Makineni, S., et al. (2026) · doi:10.1109/missf68264.2026.11522194
“The new age of digital education has redesigned the video-style classroom learning environments to inter learning digital ecosystems [1].”
Responsible AI Implementation Strategies Within Educational Administration
Das, S. D., & Bala, P. K. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-0467-8.ch010
Impact of Artificial Intelligence on Faculty Performance in Delhi-NCR
Kuhar, D., Sharma, S., Kumar, P., & Rohilla, A. (2026) · 2026 International Conference on AI Innovations and Industry (ICAIII) · doi:10.1109/icaiii69475.2026.11521751
“Another issue is privacy, as the nature of most AI technologies requires sensitive student information, increasing the likelihood of such information falling into unauthorised hands in the absence of strict safeguards [1,10].”
Soto, M. V. V. (2026) · Estudios y Perspectivas Revista Científica y Académica · doi:10.61384/r.c.a..v6i2.1864
Exploring intelligent-TPACK among university teachers: a two-phase mixed-methods study
Joshi, S. C. (2026) · Journal of Computers in Education · doi:10.1007/s40692-026-00395-0 · Cited by 1
Balancing AI ‐Driven Efficiency and Human Expertise in Higher Education
Albannai, N. A. A., Raziq, M. M., Scott‐Kennel, J., Rice, J. L., & Nusairi, A. M. A. (2026) · Knowledge and Process Management · doi:10.1002/kpm.70110
Swandi, A., Suyudi, M., Munir, F. S., Jihad, A., Samad, P., & Suryadi, R. (2026) · F1000Research · doi:10.12688/f1000research.180031.1
Vrania, E., Neofotistos, V., & Lefkos, I. (2026) · Open Education Studies · doi:10.1515/edu-2025-0141
“Data use transparency as well as students ’ rights must guide the development as well as use of such technologies (Akgun and Greenhow 2021).”
Percepciones, uso y confianza en IA generativa según género en estudiantes universitarios
Rodríguez-Rodríguez, E., Moreno‐Guzmán, M., Fernández, I. O., Hervás-Pérez, J. P., Mateos‐Aparicio, I., García‐Alonso, A., Pérez-Rodríguez, M. L., Armada, R. T. J. D. L. P., Raggio, J., Gómez-Gómez, B., et al. (2026) · GDI Revista de investigación de Género Diseño e Innovación · doi:10.63206/gdi.2026.4.3
Badr, S., & Charles, T. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7467-3.ch006
Pedagogical Innovation and Its Enemies: Prospects from Technology and AI
Andreadakis, Z. (2026) · Sustainable development goals series · doi:10.1007/978-3-032-22167-4_5
Collyer-Hoar, G., & Rubegni, E. (2026) · Participatory Design Conference · doi:10.1145/3789492.3796417
Integrating DEI and Sustainable learning: A Conceptual Framework for EFL education in Norway
Rød, A. J., & Al-kadi, A. M. T. (2026) · Norsk tidsskrift for Scholarship of Teaching and Learning · doi:10.15845/norsotl.4967
“The presence of bias therefore in AI training data, can unintentionally reflect, reinforce or worse still exacerbate existing societal prejudices (Akgun & Greenhow, 2022), in effect marginalizing communities, and creating systems that exclude rather than promote DEI.”
From Post-Hoc Regulation to Runtime Governance: Ethics and Explainability in AI-Driven Education
Saarela, M., & Shenoy, P. (2026) · International Congress on Human-Computer Interaction, Optimization and Robotic Applications (ICHORA) · doi:10.1109/ichora69329.2026.11536988
Perdana, A. (2026) · AI and Ethics · doi:10.1007/s43681-026-01186-0
“The output of an algorithm that is technically “debiased” still produces unjust outcomes when deployed in discriminatory institutions [39].”
Khajiboyev, K., Selvakumar, A., Natarajan, R., Turaev, D., & Turdiev, A. S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-6986-0.ch005
Nodira, K., Natarajan, R., Selvakumar, A., Turaev, D., & Turdiev, A. S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-6986-0.ch001
ARTIFICIAL INTELLIGENCE, ETHICS, AND LEGAL ACCOUNTABILITY IN HIGHER EDUCATION INSTITUTIONS
Srivastava, M., Sahoo, G. R., Bhatt, G., Mukherji, J., Singh, J., & Mohapatra, A. (2026) · ShodhKosh Journal of Visual and Performing Arts · doi:10.29121/shodhkosh.v7.i14s.2026.8537 · Cited by 1
Optimizing the quality of higher education with artificial intelligence
Khoiruddin, M., Martono, S., & Yanto, H. (2026) · AIP conference proceedings · doi:10.1063/5.0296944
Hlongwa, N. S., & Sithole, M. P. (2025) · Journal of Educational Studies · doi:10.59915/jes.2025.24.4.2
Sharma, S., Daftari, M. S., & Engla, M. S. (2026) · International Journal of Latest Technology in Engineering Management & Applied Science · doi:10.51583/ijltemas.2026.150500116
Asrifan, A., Rismawati, R., Rosmayanti, V., Sunra, L., Jala, J., & Amaliah, S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-1285-9.ch004
Alruwaili, H. R. (2026) · Technology and Disability · doi:10.1177/10554181261458683 · Cited by 1
Pre-Service Teachers as Game Designers for Children With Dyslexia
Rodríguez-Rivera, P., León, A. M., & Rodríguez-Ferrer, J. M. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7472-7.ch005
Junior high school student perspectives on the use of ChatGPT in music education
Weng, S. S., & Chiang, H.-C. (2026) · Computers and Education Open · doi:10.1016/j.caeo.2026.100387
Exploring college educators’ behavioral intentions towards ChatGPT: a UTAUT2 framework analysis
Assali, M. A., & O’Sullivan, K. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1824013
Li, J. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1835300
Sahoo, S. K., Sahu, S., Choudhury, B. B., Dhal, P. R., & Dhar, I. (2026) · Journal of Contemporary Decision Science · doi:10.67334/cds21202611 · Cited by 1
“Akgun & Greenhow (2022) [25] Study reviews AI applications benefit ethical challenges and teaching resources in K-12 education.”
Alghonaim, A. S. (2026) · Isagoge - Journal of Humanities and Social Sciences · doi:10.59079/isagoge.v6i1.314
SALAS, G., VILLASANTE, Y. E., CHIRINOS, G. T., et al. (2026) · ESPACIOS · doi:10.48082/espacios-a26v47n03i13
Kong, S. C., & Wang, Q. (2026) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2026.100631
Deep Learning and Neural Networks for Humanizing Conversational AI
Kosimov, O., Aarthi, S., Ravikumar, R. N., Bakhshilloevich, S. D., Odilbekovich, R. B., & Masharipova, G. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-3361-8.ch002
Design Dimensions for Planning Artificial Intelligence‐Supported Learning
Saar, M., Ljalikova, A., Alkhasawneh, S. N., & Hernández‐Leo, D. (2026) · Journal of Computer Assisted Learning · doi:10.1002/jcal.70282
Barrett-Maitland, N., Smith, J., Green, D., Benjamin, S. J., McKoy, J., & Brown, D. N. (2026) · Journal of International Education in Business · doi:10.1108/jieb-10-2024-0131
The Impact of AI on Teacher’s Profession: Beyond Boogeyman and Critique
Bagdonaitė, J., & Duoblienė, L. (2026) · SOCIAL WELFARE INTERDISCIPLINARY APPROACH · doi:10.15388/sw.2026.16.7
Chen, J., Chen, X., & ZHANG, Y. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6983024
Chen, J., Chen, X., & ZHANG, Y. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6983019
Gupta, A., Абдурахманова, М., Buzulayho, K., Xaydarova, M., Gaffarovich, N. M., Putheara, D. Y., Phearak, P. O. K., & Moreira, F. T. T. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-9883-9.ch007
Роль искусственного интеллекта в обучении школьников математике
Осинцева, М. А. (2026) · Higher education today · doi:10.18137/rnu.het.26.01.p.098
Gyedu, F. O., Partey, P. A., Arthur, F., & Opoku, E. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1838740
Farinola, T. P., & Özverir, İ. (2026) · Scientific Reports · doi:10.1038/s41598-026-59225-z
“Conversely, “black box” AI systems that provide outputs without explainable reasoning generate skepticism and lower adoption, particularly among learners with prior negative technology experiences 5,35 .”
InfluentialAhmad, I. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-4893-3.ch003
Curran, F. C., Goo, J., Thomas, C. D., Wang, P., Moylan, K., & Maharaj, S. (2026) · Educational Policy · doi:10.1177/08959048261461456
Özdemir, A., & Benzer, S. (2026) · Karaelmas Eğitim Bilimleri Dergisi · doi:10.58638/kebd.1917721
AI-Enabled Plant Recognition for Business Plan Writing in Vocational Agribusiness
Sur’ataini, A., Restian, A., & Husamah (2026) · Women Education and Social Welfare · doi:10.70211/wesw.v3i2.480
“It turns the AI result into an object of inquiry and evaluation, thereby strengthening rather than replacing students’ judgment and AI literacy [5], [6], [7].”
Nagae, Y., Zhang, L., Nouri, J., & Mannila, L. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29773-0_5
Opportunities and Tensions of AI in Special Education: Perspectives from Reddit Teacher Communities
Kim, C. K. H., Na, H., Kim, S. J., & Kim, N. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-29788-4_29
Generative AI in Secondary Education Across Diverse Populations
Dalki, T., Yemini, M., & Roll, I. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-29794-5_73
Zhou, H., Chen, C., & Xie, Y. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1868148 · Cited by 1
“Therefore, resource provision and routine training at the school level alone may be insufficient to directly eliminate preservice teachers’ concerns about the potential consequences of AI technologies (Akgun and Greenhow, 2021).”
Dziubanovska, N., & Maslii, V. (2025) · doi:10.55056/ceur-ws.org/vol-4096/paper1.pdf
“Similar observations are supported by studies by Akgün and Greenhow [14], Boulay [15] and Glover [16].”
Soft-skills as catalysts of AI augmented employability among human resources graduates in Morocco
Fifani, S., Derkaoui, R., & Rabhi, D. (2026) · Discover Education · doi:10.1007/s44217-026-01795-1
“Contemporary HR professionals increasingly interact with AI-powered applicant tracking systems, predictive analytics platforms, sentiment analysis tools, and automated workforce planning algorithms [2].”
Double-Edged Effects of AI on Overall Performance of Moroccan Students in Higher Education
Kissani, I., & Ouslimane, I. (2026) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon67543.2026.11574347
“But there also exists a parallel and much more serious issue that the cognitive offloading that AI facilitates may lead to the degradation of core capabilities like critical thinking, analytical reasoning and creative problem-solving [8], [9].”
Metadata
- Authors
- Akgun, S., & Greenhow, C.
- Affiliations
- Michigan State University (US)
- Venue
- AI and Ethics
- Published
- 2021-09-22
- Volume
- 2
- Issue
- 3
- Pages
- 431-440
- ISSN
- 2730-5953 (ISSN-L)
- 2730-5961
- PMC
- PMC8455229
- OpenAlex
- W3199189488
- Cited by
- 1,473
- References
- 43
- Citations by year
- 2026: 433 · 2025: 517 · 2024: 393 · 2023: 109 · 2022: 21
- FWCI
- 19.89
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- AI and Ethics (publishedVersion, other-oa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- michigan state university, college of education (Summer Research Fellowship)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- artificial intelligence in education, AI ethics, K-12 education, personalized learning, automated assessment, AI literacy, Code.org, MIT Media Lab, ethical dilemmas of AI
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