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 901–1,000 of 1,448
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Barca, A., & Tripaldi, M. (2024) · European Public & Social Innovation Review · doi:10.31637/epsir-2024-1523 · Cited by 1
(2025) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-3474-4.ch002 · Cited by 2
Luo, P. (2025) · International Conference on Intelligent Systems and Computational Networks (ICISCN) · doi:10.1109/iciscn64258.2025.10934565 · Cited by 1
Maigina, A., Fathurrohman, F., & Wuryandani, W. (2024) · International Journal of Elementary Education · doi:10.23887/ijee.v8i4.85994 · Cited by 7
“Many teachers consciously or unwillingly learn so as not to be left behind by the times (Akgun & Greenhow, 2022; Tomczyk, 2024).”
Qiu, Y., Ishak, N. A., & Zheng, S. (2024) · doi:10.1145/3722237.3722290 · Cited by 4
“Squirrel AI has organically integrated artificial intelligence technology into the adaptive learning system [3].”
Polinar, M. A. N., Mahipos, A. M. N., Rabanes, B. G., & Ato, J. M. U. (2025) · Diversitas Journal · doi:10.48017/dj.v10i2.3345 · Cited by 3
Comparing Human and Machine Responses to Foster In-service Teachers’ AI Literacies
Ocak, C., Han, S. C., & Caskurlu, S. (2025) · Proceedings. · doi:10.22318/icls2025.117145 · Cited by 1
Help-Seeking in Problem-Solving: Comparing Generative AI, Peers, and Teacher
Florido, H., & Hernández‐Leo, D. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-99267-4_2
Ethics and Governance of Large Language Models
Presta, G. R., & Paolella, A. (2025) · Springer eBooks · doi:10.1007/978-3-031-92285-5_10 · Cited by 1
Oubibi, M., Fute, A. Z., Hryshayeva, K., & Oubibi, A. (2025) · Future education and learning spaces · doi:10.1007/978-981-96-7937-9_5 · Cited by 1
Jiang, K. (2025) · SHS Web of Conferences · doi:10.1051/shsconf/202522201008 · Cited by 1
Using Artificial Intelligence in Primary School Mathematics Teaching
Doğan, A. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-1998-8.ch011 · Cited by 2
Sun, L., & Zheng, Y. (2026) · Journal of Research on Technology in Education · doi:10.1080/15391523.2026.2711656 · Cited by 1
Impact of AI on Student Learning Outcomes
Kayyali, M. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-8382-7.ch004 · Cited by 2
Assessing Ethical and Legal Implications for a Trustworthy Ai Development in EducationPreprint
Punzi, C., Fedele, A., & Tramacere, S. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4494528 · Cited by 1
Vidal, I. M. G., Romero-García, C., & González, A. A. R. (2024) · Research Square · doi:10.21203/rs.3.rs-4311604/v1 · Cited by 1
Vidal, I. M. G., Romero-García, C., & González, A. A. R. (2024) · Research Square · doi:10.21203/rs.3.rs-4311604/v2 · Cited by 1
AI Institutional Transformation and Challenges for ChangePreprint
Mohamed, E. A. S., & Alamin, M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5151354 · Cited by 1
The Use of Artificial Intelligence in Academic Writing: What is Ethical and What is NotPreprint
Mwita, K., & Mwilongo, N. H. (2025) · Social Science Research Network · doi:10.2139/ssrn.5243488 · Cited by 6
“Admittedly, technology has been rapidly changing which requires academicians to identify other emerging forms of malpractices in the scholarly world (Akgun & Greenhow, 2022).”
Massouti, A., Shaya, N. M., & Baroudi, S. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5385253 · Cited by 1
Rismani, E., & Köprülü, F. (2025) · Qeios · doi:10.32388/mfjsm4.2 · Cited by 1
Artificial Intelligence (AI) in Education
Anisah, T. N., & Andika, A. (2025) · Wiley eBooks · doi:10.1002/9781394411283.ch2 · Cited by 1
Lara, D. L. L., Bumbila, I. A. C., Tuarez, V. A. L., et al. (2025) · Salud Ciencia y Tecnología · doi:10.56294/saludcyt20252185 · Cited by 1
Teachers' Experiences with AI in K–12 Education: Opportunities and Challenges
Güneş Metin, K., & Çetin, M. (2026) · Journal of Educational Technology Systems · doi:10.1177/00472395261415889
Padilla, J. E., & Lee, K. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4581554
INTERACTIVE CHATBOTS: A WAY FOR THE DEVELOPMENT OF THE EDUCATIONAL SECTOR
Khandagale, H. P., & Mane, S. V. (2023) · International Journal of Engineering Applied Sciences and Technology · doi:10.33564/ijeast.2023.v08i01.034
Instructional Frameworks for Teaching Writing in the Age of AI
Nelson, E. T., & Opatz, M. O. (2023) · Advances in educational technologies and instructional design book series · doi:10.4018/978-1-6684-8661-0.ch009
Evaluating the Content of AI-Recommended YouTube Webisodes: A Focus on Sex Education in India
Jain, S., & Kaur, K. (2023) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt56998.2023.10307416
“This has clear implications in education, where AI can personalize learning, automate assessment, and offer insights into learner behavior (Akgun & Greenhow, 2021).”
BackgroundExploring Legal Aspects Related to Emerging Technologies in Tanzanian Distance Education
Ally, A. M. (2024) · JOURNAL OF ISSUES AND PRACTICE IN EDUCATION · doi:10.61538/jipe.v15i2.1387
الهادي, م. (2024) · Compunet ( The Egyptian Information Journal ) · doi:10.21608/jstc.2024.338390
Bhandari, H. P. (2024) · OCEM Journal of Management Technology & Social Sciences · doi:10.3126/ocemjmtss.v3i1.62231
Building a Digit Classifier with MNIST
Williams, J. (2024) · Mathematics Teacher Learning and Teaching PK-12 · doi:10.5951/mtlt.2023.0187
The role of artificial intelligence in activating the professional practice of digital social work
العيسوي, ح. ا. ر. (2022) · Maǧallaẗ Mustaqbal Al-ʿulūm Al-Iğtimāʿiyyaẗ · doi:10.21608/fjssj.2022.346660
تصور مقترح لتطوير تخصص علم النفس التربوي في ضوء استراتيجية التنمية المستدامة "رؤية مصر 2030"
الفيل, ح. م. ح. (2024) · Mağallaẗ Buḥūṯ Al-TarbĪyyaẗ Al-H̲āṣaẗ Wa Al-TaʿlĪm Al-Šāmil · doi:10.21608/jiser.2024.271952.1019
Gao, Z., & Wan, L. (2024) · Advances in Educational Technology and Psychology · doi:10.23977/aetp.2024.080214 · Cited by 1
“He discussed the auxiliary role of artificial intelligence in student learning and the moral challenges encountered when applying it to the field of education [1].”
BackgroundYing, L., Xu, H., Sun, J., Chen, L., Ye, Z., Ye, Q., Cheng, Z., Wu, X., Hong, X., Li, F., et al. (2024) · Research Square · doi:10.21203/rs.3.rs-3994192/v1
Maliuha, A. I. (2024) · Scientific notes of Taurida National V I Vernadsky University Series Technical Sciences · doi:10.32782/2663-5941/2024.1.1/33
Güncel Eğitim Bilimleri Araştırmaları IX
Özcan, S., Özcan, A., Pehlevan, E., Çavuş, F., Erdem, A., Mete, H., Uzal, G., KILIÇ, E., Yaşar, H., Karagücük, V., et al. (2024) · Akademisyen Kitabevi eBooks · doi:10.37609/akya.3062
A educação na era da inteligência artificial: transformações no ensino-aprendizagem
da Silva, M. L. A., Silva, K. L., Neto, J. M. N., et al. (2024) · Contribuciones a las Ciencias Sociales · doi:10.55905/revconv.17n.4-242
Vincy, S. J. (2024) · Journal of chemical health risks · doi:10.52783/jchr.v14.i2.3660
Enhancing Assessment Systems in Higher Education
Al-Amin, M., Saqui, F. Z., & Khan, M. R. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-2145-4.ch002 · Cited by 1
Yang, Y. (2024) · Journal of Electrical Systems · doi:10.52783/jes.2718
“Picture recognition is a method that uses a computer to examine a picture for distinguishing features (Akgun & Greenhow, 2022)[8].”
MethodRELEN. Educación Normal en Latinoamérica. 2023
Ahumada, N. B. P., & Rascón, O. C. A. (2024) · doi:10.46990/iquatro.2024.08.5.0
The Computer Audit for Health Affordable Housing Based on Data Mining
Li, Z., Yan, S., Cheng, X., & Kumar, S. A. (2024) · Advances in transdisciplinary engineering · doi:10.3233/atde240404
AI- ASSISSTED AND NON AI ASSISTED WRITING PERFORMANCES OF GRADE 10 STUDENTS
Bulante, K. M., MORRIS, D., Jr., BALAGAN, A., & TORRES, M. A. (2024) · Azal arts and humanities · doi:10.55687/aah.v1i1.74 · Cited by 2
Coleman, A., & Beta, K. (2024) · Contributions to management science · doi:10.1007/978-3-031-58704-7_12 · Cited by 1
Yarozkaja, L., & Aleynikova, D. V. (2024) · doi:10.29039/978-5-94472-207-2-07-2024 · Cited by 1
Salih, G., & Mohammed, F. K. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-65996-6_6 · Cited by 1
TECNOLOGIA E EDUCAÇÃO: EXPLORANDO OS EFEITOS DA DIGITALIZAÇÃO NO ENSINO SUPERIOR
Stroparo, T. R., Guerra, A. D. L. E. R., Júnior, F. P. D. C., Pires, P. A. D., Rozendo, J. F., & Vasconcelos, E. S. (2024) · Revista Missioneira · doi:10.31512/missioneira.v26i2.1812
صفر, ع. ح. (2024) · المجلة التربوية لکلية التربية بسوهاج · doi:10.21608/edusohag.2024.294761.1489
Choi, M.-S., & Kim, D. K. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-2885-9.ch008
García-Alcaráz, J. L., Díaz-Reza, J. R., & Limón-Romero, J. (2024) · Dilemas contemporáneos Educación Política y Valores · doi:10.46377/dilemas.v12i1.4284 · Cited by 3
Chat Generative Pre-Trained Transformer (ChatGPT): Boon or Bane – A Qualitative Meta-Synthesis
Palomar, E. R. C., & Sobrevega, M. E. B. (2024) · International Journal of Latest Technology in Engineering Management & Applied Science · doi:10.51583/ijltemas.2024.130723
Unveiling the Untapped Potential
GÜVEN, C., & Altınpulluk, H. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-5518-3.ch008
När AI träder in i naturvetenskaps- och teknikklassrummen: Etiska dilemman för lärare
Stolpe, K., & Höst, G. (2024) · ATENA Didaktik · doi:10.3384/atena.2024.5518
“Det kan finnas en risk att man låter sig styras alltför starkt av de förslag som man får av AI, istället för att utgå från egna värderingar och det egna professionella omdömet [10].”
Artificial intelligence as perceived by students of pedagogical specialties
Savenkov, A. I., & Voropaev, M. V. (2024) · Sovremennyj učënyj. · doi:10.58224/2541-8459-2024-5-249-263
Navigating the Role of Artificial Intelligence in Post-Secondary Educational Spaces
Sommer, T. J. (2024) · Advances in higher education and professional development book series · doi:10.4018/979-8-3693-3904-6.ch002
Kroog, K. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-72430-5_25
Smart Education Using Explainable Artificial Intelligence
Sharma, N. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-8151-9.ch004
Crivellaro, M. V. (2024) · Springer eBooks · doi:10.1007/978-3-031-67055-8_6 · Cited by 1
Artificial Intelligence Experiences of Pre-Service Preschool Teachers
Aydin, F., Budak, K. S., Polat, E. B., Engin, K., & Ulutaş, İ. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-3944-2.ch003
How Does a University’s Computer Science Strength and Location Impact Its Total ChatGPT News?
Zhan, E., Gunasekera, S., & Xu, Y. (2024) · Open Journal of Applied Sciences · doi:10.4236/ojapps.2024.1410194
Defining a Privacy Ethical Framework for Service Design
Parrilli, D. M. (2024) · Springer series in design and innovation · doi:10.1007/978-3-031-76926-9_5
Noonen, S., & Baek, J. (2024) · The Journal of Korean Association of Computer Education · doi:10.32431/kace.2024.27.4.015
Exploring the Fundamental Aspects of Artificial Intelligence: A Comprehensive Overview
Singh, A., Gulfisha, S., Koshta, A., & Mane, S. (2024) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815305753124010005
Teaching, Learning, and Coaching Using Generative AI Tools
Ramya, R. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-6170-2.ch014
AI in Education: Prioritizing Transparency and Trustworthiness
Kastania, N. P. (2024) · Springer eBooks · doi:10.1007/978-981-13-2262-4_309-1
Teaching measurement science and technology in the times of pervasive AI
Morawski, R. Z. (2024) · Measurement Sensors · doi:10.1016/j.measen.2024.101315
Volume 7 (2024) Artificial Intelligence and Responsibility
Alnajashi, A. A., DeRise, D., Frana, P. L., McGraw, D. K., Sawyer, A. G., Titareva, T., Zaini, R. M., & Zombron, A. (2024) · International Journal on Responsibility · doi:10.62365/2576-0955.1139 · Cited by 1
Adapting Translation Teaching to Global Demands: A Comprehensive Approach
Yan, W. (2024) · English Language Teaching · doi:10.5539/elt.v18n1p48
Akademisyenlerin Dijitalleşme Algısı Ölçeği’nin Geliştirilmesi
Tekir, E., & Yıldız, N. (2024) · Türk Eğitim Bilimleri Dergisi · doi:10.37217/tebd.1566386
Navío-Inglés, M., Tirado‐Olivares, S., & O’Connor-Jiménez, P. (2025) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-7185-5.ch014
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Patil, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5057398
Unveiling the Convenience and Drawbacks of Artificial Intelligence (AI) in Education
Ampong, R. L. T., Codilla, L. L., Romero, T. J., & Caparo, P. D. P. (2024) · International Conference on TVET Excellence & Development (ICTeD) · doi:10.1109/icted62334.2024.10844614 · Cited by 3
“The study by Akgün and Greenhow on addressing ethical challenges in AI in education emphasizes the importance of considering ethical implications when implementing AI technologies in educational settings [32].”
The Utilization of Artificial Intelligence in Higher Education Institutions in Germany
Bohlens, C. (2025) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-7949-3.ch012 · Cited by 1
Cioci, G. (2025) · EDUCATION SCIENCES AND SOCIETY · doi:10.3280/ess2-2024oa18439 · Cited by 4
An Intelligent Context Based Movie Recommendation System Using Bi-Directional Gated Recurrent Unit
Sundari, V. S., Al–Fatlawy, R. R., Manohara, H. T., et al. (2024) · International Conference on Integrated Intelligence and Communication Systems (ICIICS) · doi:10.1109/iciics63763.2024.10859694
Demystifying AI for Instructional Designers
Abdullah, M. (2025) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-6527-4.ch005
Using ChatGPT to Deconstruct K-12 Student Learning Outcomes for Developing Assessments and Rubrics
Bolton, N. (2025) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-6527-4.ch009
Child computer interactions: Cognitive development and segmenting unsafe video contents: A review
Zahoor, I., & Lone, S. A. (2025) · Entertainment Computing · doi:10.1016/j.entcom.2025.100931
Anthony Ogbonna, U. C. H. E., OBIORA, A. V., & Nwabudike, F. C. (2025) · British Journal of Contemporary Education · doi:10.52589/bjce-uweip6rz · Cited by 3
“AI in educational contexts is not just about technical issues but includes its implications to society regarding social justice, protection of intellectual property and individual’s privacy (Hermansyah et al., 2023; Adams et al., 2022; Akgun & Greenhow, 2021; Uche et al., 2016).”
Đạo Đức của Người Sử Dụng Trí Tuệ Nhân Tạo trong nền Giáo Dục 5.0
Bui, M. T. (2025) · Technical Education Science/Giáo dục Kỹ thuật · doi:10.54644/jte.2025.1664
Conclusion: Shaping a DIGICOMPASS Future
Rocca, S. (2024) · Springer eBooks · doi:10.1007/978-3-031-81318-4_7
Wang, Y. (2025) · Applied Mathematics and Nonlinear Sciences · doi:10.2478/amns-2025-0544
Harnessing English Medium Instruction and Artificial Intelligence for Sustainable Development Goals
Arslan, S., & Curle, S. M. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-6165-8.ch012
Tarafder, E., Khatun, S., Awais, M., Qayyum, M., Nizamani, M. M., & Wang, Y. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-24712-5.00010-5
Premawardhena, N. C. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-85652-5_26
Yu, X. (2025) · SHS Web of Conferences · doi:10.1051/shsconf/202521302042
Teaching Machine Learning in K–12 Education: Potential and Challenges
Bravo, G., Caballero, J. E. A. P., de la Cruz, J. R. R., de Ruiz, K. F. C., Purguaya, R. J. T., & Cristóbal, O. I. M. (2025) · International Conference on Pervasive Computational Technologies (ICPCT) · doi:10.1109/icpct64145.2025.10940750 · Cited by 1
“The statistical principles of techniques for creating programs that can expand and forecast from unorganized information are the main emphasis of AI instruction today [2].”
Introducing Artificial Intelligence (AI) in Social Research
Christou, P. (2025) · CABI eBooks · doi:10.1079/9781800626607.0001
Holden, M., Paisley, N., Healy, M., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5178592
Utilization of Artificial Intelligence on Educational Climate and Students’ Academic Achievement
Chanpokin, R., & Lomarda, E. Y. (2025) · Psychology and Education A Multidisciplinary Journal · doi:10.70838/pemj.350509
Current Trends in Artificial Intelligence Educational Practices
Rguig, S. (2025) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3373-2262-9.ch001
Fitrian, H. P., Rosyanti, J., Awaliyah, Y. S., Akbar, Z., & Juhaeri, S. P. (2025) · JATI (Jurnal Mahasiswa Teknik Informatika) · doi:10.36040/jati.v9i2.12928 · Cited by 1
Mosae, T., & Kaushal, R. (2024) · ShodhKosh Journal of Visual and Performing Arts · doi:10.29121/shodhkosh.v5.i7.2024.4877 · Cited by 1
“In order to meet this imperative, a diverse array of research collectives and nonprofit entities disseminate a variety of open-access resources pertaining to artificial intelligence and ethical considerations (Akgun & Greenhow, 2021).”
InfluentialRahman, Z. A., & Hamzah, A. F. (2025) · Al-Munawwarah Journal of Islamic Education · doi:10.38073/almunawwarah.v1i1.2513
Karaca, H. (2025) · e-Kafkas Eğitim Araştırmaları Dergisi · doi:10.30900/kafkasegt.1534014
(2025) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-83549-946-720251015
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-09-28, under version 3 of the claim definition (prompt extract-v3).
A second model labelled each claim on 2026-09-28 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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