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 101–200 of 1,448
Rezai, A., Soyoof, A., & Reynolds, B. L. (2024) · European Journal of Education · doi:10.1111/ejed.12752 · Cited by 86
Baker, B., Mills, K. A., McDonald, P., & Wang, L. (2023) · Teachers College Record The Voice of Scholarship in Education · doi:10.1177/01614681231191291 · Cited by 71
Using AI to Support Special Education Teacher Workload
Goldman, S. R., Taylor, J., Carreon, A., & Smith, S. J. (2024) · Journal of Special Education Technology · doi:10.1177/01626434241257240 · Cited by 66
“Given that AI systems are trained on publicly available data from the internet, the responses from the AI may carry implicit bias stemming from existing historical and societal biases (Akgun & Greenhow, 2022).”
Ramnarain, U. D., Ogegbo, A. A., Penn, M., Ojetunde, S. M., & Mdlalose, N. (2024) · Journal of Science Education and Technology · doi:10.1007/s10956-024-10159-z · Cited by 100
“…with AI integration in school education is privacy concern that appears when surveillance or tracking systems in the classroom gather detailed information regarding students’ and teachers’ preferences and actions without their consent (Akgun & Greenhow, 2022; Stockman and Nottingham, 2022).”
Alam, A., & Mohanty, A. (2022) · Communications in Computer and Information Science · doi:10.1007/978-3-031-23233-6_2 · Cited by 62
Waltzer, T., Cox, R. L., & Heyman, G. D. (2023) · Human Behavior and Emerging Technologies · doi:10.1155/2023/1923981 · Cited by 61
“Another far-reaching set of questions concerns its effect on education, including its potential to promote critical thinking [8] or identify which teaching approaches work best in different contexts [9], as well as the possibility that it will exacerbate social problems such as discrimination in education [10, 11].”
BackgroundDeveloping secondary school English language learners' productive and critical use of ChatGPT
Javier, D. R. C., & Moorhouse, B. L. (2023) · TESOL Journal · doi:10.1002/tesj.755 · Cited by 63
ChatGPT and other AI Users: Innovative and Creative Utilitarian Value and Mindset Shift
Duhaylungsod, A. V., & Chavez, J. V. (2023) · Journal of Namibian Studies : History Politics Culture · doi:10.59670/jns.v33i.2791 · Cited by 59
A Systematic Review of AI Ethics in Education
Yan, Y.-Y., Liu, H., & Chau, T. (2025) · Journal of Global Information Management · doi:10.4018/jgim.386381 · Cited by 39
Artificial Intelligence in Endodontic Education
Aminoshariae, A., Nosrat, A., Nagendrababu, V., Dianat, O., Mohammad‐Rahimi, H., O'Keefe, A. W., & Setzer, F. C. (2024) · Journal of Endodontics · doi:10.1016/j.joen.2024.02.011 · Cited by 72
Deák, C., & Kumar, B. (2024) · Education Sciences · doi:10.3390/educsci14030226 · Cited by 87
Wang, X., Xu, X., Zhang, Y., Hao, S., & Weng, J. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-04168-x · Cited by 76
Lan, G., Feng, X., Du, S., Song, F., & Xiao, Q. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13427-6 · Cited by 82
“Additionally, GenAI tools designed for language learning might struggle to accommodate various accents and dialects, potentially disadvan-taging non-native speakers (Akgun & Greenhow, 2022).”
InfluentialTang, Q., Deng, W., Huang, Y., Wang, S., & Zhang, H. (2025) · Journal of Computer Assisted Learning · doi:10.1111/jcal.70027 · Cited by 55
Celik, I., Gedrimiene, E., Siklander, S., & Muukkonen, H. (2024) · Australasian Journal of Educational Technology · doi:10.14742/ajet.9069 · Cited by 59
“In order to answer RQ2, we coded the articles by focusing on four AI-based tools – chatbots, AI-based dashboards, automated grading systems and intelligent tutoring systems – which are reported as the most widely used (Akgun & Greenhow, 2022; Celik, 2023b).”
BackgroundMethodMultimodality of AI for Education: Toward Artificial General Intelligence
Lee, G., Shi, L., Latif, E., Gao, Y., Bewersdorff, A., Nyaaba, M., Guo, S., Liu, Z., Mai, G., Liu, T., et al. (2025) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2025.3574466 · Cited by 199
“For example, researchers in AI-enhanced e-learning areinterestedininvestigatingethicalissues,suchasuserconsent, equity and diversity, accessibility, identity and confidentiality, and student privacy [159].”
Xie, Y., Wu, S., & Chakravarty, S. (2023) · Conference on Information Technology Education · doi:10.1145/3585059.3611449 · Cited by 54
An Introduction to Generative AI Tools for Education 2030
Sandhu, R., Channi, H. K., Ghai, D., Cheema, G. S., & Kaur, M. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-2440-0.ch001 · Cited by 59
The effect of AI-powered chatbots in social studies education
Yeti̇şensoy, O., & Karaduman, H. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12485-6 · Cited by 73
“They also enable more effective teaching, bringing a series of benefits that are difficult to obtain otherwise (Akgun & Greenhow, 2022; Kabudi et al., 2021; Yang et al., 2022).”
Tlili, A., Saqer, K., Salha, S. H., & Huang, R. (2025) · Information Development · doi:10.1177/02666669241304407 · Cited by 92
Henadirage, A., & Gunarathne, N. (2025) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-024-00439-5 · Cited by 48
“In line with the literature, data privacy (Zawacki-Richter et al., 2019; Akgun & Greenhow, 2022; Salas-Pilco & Yang, 2022; Wu et al., 2024) and the digital divide (Mogaji & Jain, 2020; Ndofirepi et al., 2020; Swamy, 2023) present significant challenges for many universities in Global South nations.”
InfluentialTan, S. C., Chen, W., & Chua, B. L. (2023) · Learning Research and Practice · doi:10.1080/23735082.2023.2258895 · Cited by 55
“Controversies surrounding the use of AI for education are not new and have been gaining attention (Akgun & Greenho, 2021).”
BackgroundLabrague, L. J., & Sabei, S. A. (2025) · Teaching and Learning in Nursing · doi:10.1016/j.teln.2024.11.010 · Cited by 48
Doğan, S., Nalbantoğlu, Ü. Y., Çelik, İ., & Dogan, N. (2025) · Professional Development in Education · doi:10.1080/19415257.2025.2454457 · Cited by 102
Analyzing the Impact of AI Tools on Student Study Habits and Academic Performance
Ward, B., Bhati, D., Neha, F., & Guercio, A. (2025) · 2025 IEEE 15th Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc62904.2025.10903692 · Cited by 48
“However, several barriers—such as privacy concerns, integration challenges, costs, and limited faculty support—continue to hinder the widespread adoption of AI tools in educational environments [1], [2], [3], [4].”
Bhaskar, P., Misra, P., & Chopra, G. (2024) · International Journal of Information and Learning Technology · doi:10.1108/ijilt-11-2023-0220 · Cited by 60
Preparing Educators to Teach and Create With Generative Artificial Intelligence
MacDowell, P., Moskalyk, K., Korchinski, K., & Morrison, D. (2024) · Canadian Journal of Learning and Technology · doi:10.21432/cjlt28606 · Cited by 66
“Consequently, we are designing a new module including deep fakes, misinformation, and social manipulation (Akgun & Greenhow, 2022; Celik et al., 2022).”
BackgroundMethodPei, B., Lu, J., & Jing, X. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100406 · Cited by 78
An Evaluation of the Use of Artificial Intelligence Applications in Online Education
Tonbuloğlu, B. (2023) · Journal of Educational Technology and Online Learning · doi:10.31681/jetol.1335906 · Cited by 54
“The ethical and societal disadvantages of AI systems can often be ignored in K-12 education contexts and it is important to identify these challenges (Akgun & Greenhow, 2021).”
Giray, L., Silos, P. Y. L. D., Adornado, A., Buelo, R. J. V., Galas, E. M., Reyes-Chua, E., Santiago, C. S., & Ulanday, M. L. P. (2024) · Internet Reference Services Quarterly · doi:10.1080/10875301.2024.2352746 · Cited by 80
Ghimire, A., & Edwards, J. (2024) · Intermountain Engineering, Technology and Computing (IETC) · doi:10.1109/ietc61393.2024.10564292 · Cited by 116
“Akgun and Greenhow emphasized the ethical considerations necessary for AI deployment in K-12 settings, advocating for principles like transparency and inclusiveness [9].”
BackgroundVisualizing the knowledge mapping of artificial intelligence in education: A systematic review
Qin, Q., & Zhang, S. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-13076-1 · Cited by 58
“Akgun and Greenhow (2022) discussed technologies such as personalized. learning platforms, automated assessment systems, and facial recognition systems.”
Gamifying Learning with AI: A Pathway to 21st-Century Skills
Niño, J. R. G., Arias-Delgado, L., Chiappe, A., & González, E. O. (2024) · Journal of Research in Childhood Education · doi:10.1080/02568543.2024.2421974 · Cited by 44
“…et al., 2020; Yu et al., 2021), improve motivation in teaching-learning processes (López & Tucker, 2019), support teachers with tools to strengthen expected learning (Zhai et al., 2021), and face challenges related to privacy, surveillance, bias, and discrimination (Akgun & Greenhow, 2022).”
Kelley, M. J., & Wenzel, T. (2025) · Education Sciences · doi:10.3390/educsci15060659 · Cited by 44
“It can enhance educators’ efficiency by streamlining administrative tasks such as grading and feedback (Zawacki-Richter et al., 2019) and can support instructional planning, design, and adaptation (Akgun & Greenhow, 2022).”
Santiago, C. S., Embang, S. I., Acanto, R. B., Ambojia, K. W. P., Aperocho, M. D. B., Balilo, B. B., Cahapin, E. L., Conlu, M. T. N., Lausa, S. M., Laput, E. Y., et al. (2023) · International Journal of Learning Teaching and Educational Research · doi:10.26803/ijlter.22.11.14 · Cited by 61
AI in Medical Education Curriculum: The Future of Healthcare Learning
Naqvi, W. M., Sundus, H., Mishra, G., Muthukrishnan, R., & Kandakurti, P. K. (2024) · European Journal of Therapeutics · doi:10.58600/eurjther1995 · Cited by 58
Fedele, A., Punzi, C., & Tramacere, S. (2024) · Computer law & security review · doi:10.1016/j.clsr.2024.105986 · Cited by 67
Öğrencilerin Yapay Zeka Okuryazarlığı Üzerine Bir İnceleme
Elçiçek, M. (2024) · Bilgi ve İletişim Teknolojileri Dergisi · doi:10.53694/bited.1460106 · Cited by 44
Fortuna, A., Prasetya, F., Samala, A. D., Rawas, S., Criollo-C, S., Kaya, D., Raihan, M. F. M., Andriani, W., Safitri, D., & Nabawi, R. A. (2025) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2025.102114 · Cited by 98
AI Perils in Education: Exploring Ethical Concerns
Salloum, S. A. (2024) · Studies in big data · doi:10.1007/978-3-031-52280-2_43 · Cited by 42
Swindell, A., Greeley, L., Farag, A., & Verdone, B. (2024) · Online Learning · doi:10.24059/olj.v28i2.4438 · Cited by 45
“While some research has explored the ethical implications and imagined futures of AI in education (Akgun & Greenhow, 2022; Nguyen et al., 2023), there is limited consensus or direction for classroom use of AI specifically.”
BackgroundDelcker, J., Heil, J., & Ifenthaler, D. (2024) · Educational Technology Research and Development · doi:10.1007/s11423-024-10418-1 · Cited by 49
“AI in education is defined as a combination of „machine learning, algorithm productions, and natural language processing “ (Akgun and Greenhow, 2021, p.1) with the potential to reduce teachers ‘ workload, contextualize students ‘ learning, (semi-)automate assessments (Ifenthaler et al., 2018), and…”
Exploring University Students Perceptions of Generative Artificial Intelligence in Education
Bucharest University of Economic Studies, tala, M. L., Muller, C. N., Hochschule Fresenius, B., Nastase, I. A., Bucharest University of Economic Studies, State, O., Bucharest University of Economic Studies, Gheorghe, G., & Bucharest University of Economic Studies (2024) · Amfiteatru Economic · doi:10.24818/ea/2024/65/71 · Cited by 42
A survey study of chinese teachers’ continuous intentions to teach artificial intelligence
Chai, C. S., Liang, S., & Wang, X. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-12430-z · Cited by 50
“The necessity to teach AI ethics is generally recognized as an important specialized topic of AI education (Akgun & Greenhow, 2022; Henry et al., 2021; Long & Magerko, 2020; Sun et al., 2023).”
Exploring factors influencing educators’ adoption of ChatGPT: a mixed method approach
Hidayat-ur-Rehman, I., & Ibrahim, Y. (2023) · Interactive Technology and Smart Education · doi:10.1108/itse-07-2023-0127 · Cited by 59
Rahimi, A. R., & Sevilla‐Pavón, A. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100258 · Cited by 70
Wu, H., Li, D., & Mo, X. (2025) · Education and Information Technologies · doi:10.1007/s10639-024-13312-8 · Cited by 55
“When students are aware of potential AI risks, they tend to adopt responsible practices, critically evaluate AI outputs, and avoid over-reliance on these tools (Akgun & Greenhow, 2022).”
Teaching and learning with AI: an Integrated AI-Oriented Pedagogical Model
Creely, E., & Carabott, K. (2025) · The Australian Educational Researcher · doi:10.1007/s13384-025-00913-6 · Cited by 86
“On the other hand, it is viewed as a significant threat to education, particularly concerning ethical, responsible and inclusive use, especially given its ability to generate content (writing and graphical materials) in mere seconds (Akgun & Greenhow, 2022; Sullivan et al., 2023).”
A Synthesis of AI in higher education: Shaping the future
Jacques, P. H., Moss, H. K., & Garger, J. (2024) · Journal of Behavioral and Applied Management · doi:10.21818/001c.122146 · Cited by 43
“AI algorithms thus perpetuate and amplify biases in education, affecting student evaluations, admissions decisions, and learning opportunities (Akgun & Greenhow, 2021; Crawford & Paglen, 2021).”
BackgroundJang, M. (2024) · Informatics · doi:10.3390/informatics11030054 · Cited by 55
King, S. A., Boyer, J., Bell, T., & Estapa, A. T. (2022) · JMIR Serious Games · doi:10.2196/41097 · Cited by 51
Artificial Intelligence in Education - Ethical framework
Gartner, S., & Krasna, M. (2023) · Mediterranean Conference on Embedded Computing · doi:10.1109/meco58584.2023.10155012 · Cited by 25
Evaluating generative AI integration in Saudi Arabian education: a mixed-methods study
Alammari, A. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.1879 · Cited by 65
Zhou, Q., Hashim, H. B., & Sulaiman, N. A. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13401-2 · Cited by 57
“The integration of AI technology appears to be a promising solution to streamline and enhance these instructional efforts (Ilieva, 2023; Akgün & Greenhow, 2021 ).”
Polat, E. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13548-y · Cited by 51
Role of artificial intelligence in education: A conceptual review
Tahir, M. S., Hassan, F. D., & Shagoo, M. R. (2024) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2024.22.1.1217 · Cited by 54
“The concept of AI is about the combination of applications of machine learning, deep learning, algorithm production, and natural language processing (Akgun and Greenhow, 2022).”
BackgroundRETRACTED: ChatGPT in the classroom: navigating the generative AI wave in management education
Leelavathi, R., & Surendhranatha, R. C. (2024) · Journal of Research in Innovative Teaching & Learning · doi:10.1108/jrit-01-2024-0017 · Cited by 54
RetractedAn Insight into Cultural Competence and Ethics in K-12 Artificial Intelligence Education
Sanusi, I. T., & Olaleye, S. A. (2022) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon52537.2022.9766818 · Cited by 48
The Role of Artificial Intelligence in Shaping the Future of Education: Opportunities and Challenges
Іванашко, О., Козак, А., Knysh, T., & Honchar, K. (2024) · Futurity Education · doi:10.57125/fed.2024.03.25.08 · Cited by 60
“At number of findings concern ethical considerations that concern the use of AI in education, such as data collection, the use of personal information, and the potential impact on students’ independent work (Akgun & Greenhow, 2022; Grubaugh et al., 2023).”
BackgroundPoláková, P., & Klímová, B. (2024) · Cogent Education · doi:10.1080/2331186x.2024.2355385 · Cited by 72
“These concerns highlight the need for educators to address ethical considerations, such as privacy, surveillance, autonomy, bias, and discrimination, to ensure the responsible and equitable use of AI in education (Akgun & Greenhow, 2022; Klimova et al., 2023; Schmidt & Strassner, 2022).”
Chen, J. J. (2025) · AI Brain and Child · doi:10.1007/s44436-025-00002-6 · Cited by 37
“…opportunities that cover essential topics, including strategies for effectively integrating AI into teaching practices and guidelines for addressing ethical concerns to better navigate and mitigate them while promoting AI literacy in children (Akgun & Greenhow, 2022; Chen, 2024; Chen & Lin, 2024).”
Rueda de la Pedagogía para la Inteligencia Artificial: adaptación de la Rueda de Carrington
Jiménez-García, E., Orenes-Martínez, N., & Fraile, L. A. L. (2023) · RIED Revista Iberoamericana de Educación a Distancia · doi:10.5944/ried.27.1.37622 · Cited by 74
Kayyali, M. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-7220-3.ch013 · Cited by 34
Multi-stakeholder perspective on responsible artificial intelligence and acceptability in education
Karran, A. J., Charland, P., Martineau, J. T., de Guinea, A. O., Lesage, A., Sénécal, S., & Léger, P. (2025) · npj Science of Learning · doi:10.1038/s41539-025-00333-2 · Cited by 76
Understanding EFL student writers’ metacognitive awareness in utilizing ChatGPT
Teng, M. F. (2025) · System · doi:10.1016/j.system.2025.103848 · Cited by 49
Maphosa, M., Doorsamy, W., & Paul, B. S. (2023) · IEEE Access · doi:10.1109/access.2023.3277225 · Cited by 33
Güneyli, A., Burgul, N. S., Dericioğlu, S., Cenkova, N., Becan, S., Şimşek, Ş. E., & Güneralp, H. (2024) · European Journal of Investigation in Health, Psychology and Education · doi:10.3390/ejihpe14080156 · Cited by 65
“Moreover, integrating AI literacy into the curriculum can help students understand both the capabilities and risks of AI, fostering a generation of informed and responsible AI users [74,75].”
BackgroundTechnology in education: Addressing legal and governance challenges in the digital era
Li, Z., & Zhang, W. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-13036-9 · Cited by 33
“As educational institutions increasingly utilize these technologies to enhance learning experiences and administrative processes, various ethical dilemmas arise (Akgun & Greenhow, 2022 ).”
Wong, L. P. W. (2024) · Merits · doi:10.3390/merits4040027 · Cited by 45
“Recent findings suggest that AI has both positive and negative impacts on student development [36,37].”
Overview of Current AI Technologies in Education
Hubballi, R. B., Selvakumar, P., Seenivasan, R., Aradhya, S. G. B., Dinesh, N., & Seelam, P. K. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-8744-3.ch001 · Cited by 33
Teacher Experiences with AI-based Educational Tools
Arvin, N., Hoseinabady, M., Bayat, B., & Zahmatkesh, E. (2023) · AI and Tech in Behavioral and Social Sciences · doi:10.61838/kman.aitech.1.2.5 · Cited by 68
Code suggestions and explanations in programming learning: Use of ChatGPT and performance
Park, A., & Kim, T.-K. (2024) · The International Journal of Management Education · doi:10.1016/j.ijme.2024.101119 · Cited by 48
Imagining the future of artificial intelligence in education: a review of social science fiction
Gidiotis, I., & Hrastinski, S. (2024) · Learning Media and Technology · doi:10.1080/17439884.2024.2365829 · Cited by 31
“More specifically, risks for students’ privacy and autonomy (Akgun and Greenhow 2022; du Boulay 2022; Swartz and McElroy 2023) as well as biases that can occur within large language models (LLMs) have been emphasised in the literature (Ferrara 2023; Arora et al. 2023).”
Yu, J. H., Chauhan, D., Iqbal, R. A., & Yeoh, E. (2024) · Educational Technology Research and Development · doi:10.1007/s11423-024-10425-2 · Cited by 43
“However, the integration of AI in education presents challenges related to data privacy, security, and ethics (Akgun & Greenhow, 2022).”
Almogren, A. S., Al-Rahmi, W. M., & Dahri, N. A. (2024) · IEEE Access · doi:10.1109/access.2024.3498047 · Cited by 47
“Studies by [135], [184], and [185] have shown that AI allows students to identify and address learning gaps, improving learning efficiency promptly.”
Castro, A., Díaz, B., Aguilera, C., Prat, M., & Herting, D. C. (2025) · Sustainability · doi:10.3390/su17062748 · Cited by 66
“Since Alan Turing introduced the idea of intelligent systems, various definitions have been proposed to define AI systems in education (e.g., [46–48]).”
Artificial intelligence (AI) and ethical concerns: a review and research agenda
Ghosh, M. (2025) · Cogent Business & Management · doi:10.1080/23311975.2025.2551809 · Cited by 46
“…handle extensive data, including diverse student records and other academic and non-academic information. therefore, it is crucial to implement data protection measures to prevent unauthorised utilisation or exploitation of this data and information (akgun & greenhow, 2022 ; stahl & Wright, 2018).”
Learning Analytics in the Era of Large Language Models
Mazzullo, E., Bulut, O., Wongvorachan, T., & Tan, B. (2023) · Analytics · doi:10.3390/analytics2040046 · Cited by 34
Abdelaal, N. M., & Sawi, I. A. (2024) · Australian Journal of Applied Linguistics · doi:10.29140/ajal.v7n1.1309 · Cited by 52
Unleashing the transformers: NLP models detect AI writing in education
Campino, J. (2024) · Journal of Computers in Education · doi:10.1007/s40692-024-00325-y · Cited by 39
“Firstly, the issue of bias arises, as AI algorithms may exhibit biases leading to discriminatory outcomes (Akgun & Greenhow, 2022).”
Artificial Intelligence in Curriculum Design: A Data-Driven Approach to Higher Education Innovation
Chu, T. S., & Ashraf, M. (2025) · Knowledge · doi:10.3390/knowledge5030014 · Cited by 42
“• The integration of AI-enhanced learning environments from the 1960s, with rapid acceleration in the last decade [1,4].”
InfluentialEthical Considerations in Using AI in Educational Research
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Moreira-Chóez, J. S., Barzola, K. E. G., de Rodríguez, T. M. L., García, Á. R. S., Mendoza, J. C. C., & Barcia, L. A. C. (2024) · Frontiers in Education · doi:10.3389/feduc.2024.1425487 · Cited by 78
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“Artificial intelligence (AI) and AI applications are transforming educational tools by bringing together machine learning, algorithm generation and natural language processing applications (Akgun & Greenhow, 2022).”
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“29 However, we purposefully selected student-generated reflections that used more general language when discussing the experience and because of that, these reflections became more similar to ChatGPT-generated reflections, which tend to use more general language or somewhat unspecific accounts…”
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“With the rapid development of modern network technology and mobile communication technology, Akgun and Greenhow [22] have not only changed people’s life and work but also changed the way of education and knowledge acquisition [22].”
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“In the era of Education 4.0, it is imperative to give ethical concerns the utmost importance and establish unambiguous ethical standards for using technology (Akgun and Greenhow, 2022; Kong et al., 2023; Grassini, 2023).”
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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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