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Cited by 1,473Artificial Intelligence in EducationEthics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Artificial intelligence in education: Addressing ethical challenges in K-12 settings

Open access
Akgun, Selin, Greenhow, Christine·2022·AI and Ethics
DOI 10.1007/s43681-021-00096-7PMC PMC8455229
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Abstract

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.

Integrity signals

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Crossref · 10.1007/s43681-021-00096-7

Cites

43 of 43 references verified0 citation errors · 0 not checkable

Statistics

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Claims the paper makes

7 of 69 cited sources checked · 1 supported · 0 flagged · accuracy not yet measured

  • Premisecitation

    Algorithms embody the values of their creators, who occupy positions of power.

    “In essence, algorithms reflect the values of their builders who hold positions of power [26].”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [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) source

      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)

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  • Premisecitation

    Current personalized learning platforms lack models of students' social, emotional, and motivational states.

    “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].”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [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) source

      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)

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  • Premisecitation

    Prior studies on AI in education offer guidance for helping students comprehend and apply AI.

    “Existing research on AI in education provides insight on supporting students’ understanding and use of AI [2, 13]”
    Quote in the paperNot a substantial claim1 of 2 sources checked
    • cites [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.

    • cites [13] Borenstein, J., Howard, A.: Emerging challenges in AI and the need for AI ethics education. AI Ethics 1, 61–65 (2020) source

      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)

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  • Premisecitation

    Algorithm-driven predictive systems threaten the autonomy of students and teachers and their capacity to direct their own lives.

    “Predictive systems which are powered by algorithms jeopardize students and teachers’ autonomy and their ability to govern their own life [46, 47].”
    Quote in the paperSubstantial claim1 of 2 sources checked
    • cites [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) source

      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)

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    • cites [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
  • Premisecitation

    Instruction delivered through personalized learning systems produced higher test scores than conventional teacher-led instruction.

    “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].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [36] Ladson-Billings, G.: Toward a theory of culturally relevant pedagogy. Am. Educ. Res. J. 32(3), 465–491 (1995) source
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    Premisecitation

    Algorithms are not isolated; they both influence and are influenced by cultural, social, institutional, and political forces.

    “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].”
    Quote in the paperSubstantial claim1 of 2 sources checked
    • cites [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.

    • cites [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

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    Premisecitation

    AI tools can help teach mixed-ability classes, give learners prompt and detailed feedback, and reduce teachers' workload.

    “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].”
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    • cites [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) source

      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)

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    • cites [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

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    Premisecitation

    AI systems can expand K-12 educational capacity and aid the social and cognitive growth of students and teachers.

    “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].”
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    • cites [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.

    • cites [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) source

      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)

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    Premisecitation

    The main dangers of using algorithms in K-12 are reinforcing systemic bias and discrimination, worsening unfairness for disadvantaged students, and intensifying racism, sexism, xenophobia, and other inequities.

    “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].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [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.

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    Premisecitation

    Georgia State University used a text-based chatbot named Pounce to assist students with registration, admissions, financial aid, and administrative tasks.

    “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].”
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    • cites [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

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    Interpretationcitation

    Personalized learning systems are a promising distance-learning approach that may transform future K-12 teaching.

    “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].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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.

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    Interpretationreasoning

    AI applications can improve educational experiences for students and teachers and help solve instructional problems.

    “In summary, applications of AI can positively impact students’ and teachers’ educational experiences and help them address instructional challenges and concerns.”
    Quote in the paperSubstantial claim

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    Premisecitation

    Facial recognition systems give information about student behavior during learning, enabling teacher intervention that supports learner-centered teaching and boosts engagement.

    “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].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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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    Interpretationreasoning

    Automated scoring systems may handle complex teaching contexts and help students learn by giving feedback and guidance for revising writing.

    “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.”
    Quote in the paperNot a substantial claim

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    Premisecitation

    More than 500 universities have used Gradescope to create and streamline scoring and assessment.

    “On the other hand, a tool called “Gradescope” has been used by over 500 universities to develop and streamline scoring and assessment [12].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Researchers stress that social networking sites can extend learning beyond the classroom, track student well-being, and strengthen student-teacher relationships.

    “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].”
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    • cites [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

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Using social media in education can encourage active learning, teamwork, and ties to outside communities.

    “They point out that the integration of social media can foster students’ active learning, collaboration skills, and connections with communities beyond the classroom [6].”
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    • cites [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

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    Premisecitation

    Social media are becoming more common in K-12 and subject students' activities to intelligent algorithms.

    “Social media are increasingly integrated into K-12 education [7] and subordinate learners’ activities to intelligent algorithm systems [17].”
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    • cites [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
    • cites [17] Cheney-Lippold, J.: We are data: algorithms and the making of our digital selves. New York University Press, New York (2017) source

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    Interpretationreasoning

    AI is only one instrument among many available to teachers.

    “Although AI can be a time-saving and cognitive aide for teachers, it is but one tool in the teachers’ toolkit.”
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    • Rests on an assumption: Students have a wide range of learning styles and needs.

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    Premisecitation

    AI is used in education in multiple ways, including systems for personalized learning, automated assessment, and facial recognition.

    “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].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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.

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    Premisecitation

    For a future inclusive and diverse public to take part in AI development, K-12 students and teachers need opportunities to learn AI and ethics through curricula and professional development.

    “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]”
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    • cites [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.

    • cites [58] Zimmerman, M.: Teaching AI: exploring new frontiers for learning. International Society for Technology in Education, Portland (2018)
    • 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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    Premisecitation

    Most research concerns university students' engagement with algorithmic bias ethics, while few studies examine how to teach AI ethics in K-12.

    “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.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [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.

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    Premisecitation

    The literature offers limited guidance and few studies on helping K-12 students and teachers understand the social, cultural, and ethical aspects of AI.

    “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].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [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.

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    Premisecitation

    Predictive analytics can identify university students who may fail or drop a course, so instructors can step in.

    “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].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    AI is prominent in science, engineering, and technology and appears in education via machine learning and algorithms.

    “AI dominates the fields of science, engineering, and technology, but also is present in education through machine-learning systems and algorithm productions [43].”
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    • cites [43] Naqvi, A.: Artificial intelligence for audit, forensic accounting, and valuation: a strategic perspective. Wiley (2020) source

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    The 'Learning and Algorithmic Bias' unit has learners build a cat-versus-dog classifier with two datasets-one skewed toward cats, one balanced-then compare accuracy and discuss fairness.

    “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].”
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    • cites [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.

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    Premisecitation

    For Turkish phrases meaning 'She/he is a nurse' and 'She/he is a doctor,' Google Translate produced feminine and masculine forms respectively.

    “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].”
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    • cites [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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    Premisecitation

    The next workshop, 'Designing ads with transparency,' covers advertising's purpose and effects, malware and adware, and YouTube ad elements, with students making transparent ads.

    “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].”
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    • cites [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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    In summer 2020, the pandemic led to cancellation of England's A-level and GCSE exams.

    “England’s A-level and GCSE secondary level examinations were cancelled due to the pandemic in the summer of 2020 [1, 57].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [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.

    • cites [57] Weale, S., Stewart, H.: A-level and GCSE results in England to be based on teacher assessments in U-turn. The Guardian, (2020).

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    This curriculum helps teachers lead students in learning AI technical terms and the ethical and social consequences of AI.

    “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].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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.

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    Premisereasoning

    That episode showed the grade distribution benefiting private or independent school pupils, with underrepresented groups suffering most.

    “it revealed how the score distribution favored students who attended private or independent schools, while students from underrepresented groups were hit hardest.”
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    Premisecitation

    AI systems reduce introspective and independent thought, diminishing people's agency.

    “people’s agency diminishes as AI systems reduce introspective and independent thought [55].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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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    Premisecitation

    A key objective of the MIT curriculum is teaching AI fundamentals-algorithms, datasets, supervised learning-while emphasizing algorithmic bias.

    “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].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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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    Premisecitation

    In the 'Algorithms as Opinions' investigation, students learn that algorithms can have various motives and goals.

    “In this way, students are able to figure out that algorithms can have various motives and goals.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    In the 'Ethical Matrix' activity, students identify stakeholders and see how their values and opinions are embedded in algorithms.

    “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.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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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    Interpretationreasoning

    Observing and following students' online exchanges and behavior could reduce their engagement in class and make them hesitant to claim their ideas.

    “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.”
    Quote in the paperSubstantial claim

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    Premisecitation

    AI tracking systems not only monitor activities but also determine users' future preferences and actions.

    “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].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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

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    Premisecitation

    Violations by AI technology companies regarding data access and security heighten public privacy worries, even though laws and standards exist to protect sensitive data.

    “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].”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [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
    • cites [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

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Code.org, a nonprofit aiming to broaden computer science participation, is backed by Microsoft, Facebook, Amazon, Google, and other technology firms.

    “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].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The 'AI Bingo' exercise has students determine the prediction and dataset of a chosen AI system.

    “In their AI Bingo chart, students try to identify what prediction the selected AI system makes and what dataset it uses.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    The last workshop, 'Designing Privacy in Social Media Platforms,' addresses YouTube, design, civics, and data privacy; students devise solutions for online consent and study COPPA.

    “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].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    In the initial 'Mystery YouTube Viewer' workshop, learners consider privacy and data by viewing YouTube's homepage as an unknown user and guessing about the user.

    “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.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Various scholars have studied social media in education and its effects on learning and scholarly communication.

    “Different scholars have examined the role of social media in education, describing its impact on student and teacher learning and scholarly communication [6].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    This free tutorial has learners sort items into fish and non-fish to clean the ocean, add to training data, and talk about machine learning and biased data.

    “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.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The Personal Robots Group at MIT Media Lab provides a free workshop series for ages 7-14 on data privacy and creating privacy features.

    “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.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [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.

40 of 40

  • 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.”
8 of 8

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?

  • AI systems often require access to vast amounts of student data, including academic performance, behavioral patterns, and personal information.

    “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

    The Ethical concerns and potential risks section describes AI systems accessing personal data and tracking students’ actions, preferences, and learning performance; the Benefits section also discusses systems that assess student work. These passages support the types of data named, but do not establish that AI systems often require vast amounts of it. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Educational accessibility refers to GenAI's ability to provide learning opportunities to students with limited resources, allowing equitable access to personalized content.

    “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

    The “Personalized learning systems” section says AI systems give students access to materials tailored to their learning needs. It does not describe GenAI specifically or establish that these systems provide students with limited resources equitable access to personalized content. Page numbers were not available in our copy. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • Personalization of learning is manifested through GenAI's ability to adapt educational content and methods to the specific needs of each student.

    “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

    The “Personalized learning systems” section says AI platforms provide learning materials based on individual needs and adapt tools and materials to support students’ growth. It does not establish that generative AI (GenAI) specifically adapts educational content and methods for each student. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • The growing use of AI in higher education raises concerns about data privacy and security.

    “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

    The section “Ethical concerns and potential risks of AI applications in education” identifies data privacy and security as concerns arising from AI use, but it discusses them in K–12 education. It does not establish the claim specifically for higher education. No page number is available in our copy. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • Current discussions on trustworthy artificial intelligence in education highlight the importance of ethical awareness, responsible data use, and critical engagement with AI-driven systems in educational contexts.

    “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

Self-check

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.

  • Code.org activity has two different titles

    Minor

    The same Code.org activity is called "AI and Oceans" in the overview but "AI for Oceans" in its dedicated section.

1 of 1

Cites

Flagged first, then by citations

Research works printed but not linked: 4

  1. 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)
  2. Citron, D.K., Pasquale, F.A.: The scored society: due process for automated predictions. Wash. Law Rev. 89, 1–33 (2014)
  3. 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
  4. 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

Related

Authors
Akgun, S., & Greenhow, C.
Affiliations
Michigan State University (US)
ORCID
Venue
AI and Ethics
Publisher
Springer Science and Business Media LLC
Published
2021-09-22
Volume
2
Issue
3
Pages
431-440
ISSN
  • 2730-5953 (ISSN-L)
  • 2730-5961
DOI
10.1007/s43681-021-00096-7
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
Licence
Locations
  • AI and Ethics (publishedVersion, other-oa)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
michigan state university, college of education (Summer Research Fellowship)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Social Sciences › Safety Research › 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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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.

Claims read under definition version 3 (extract-v3) on 2026-09-28.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-09-28.

Cited sources are checked by gpt-6-sol.

Statistics and arithmetic are recomputed, not judged.

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