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Ethical and Pedagogical Impacts of AI in Education

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Artificial Intelligence in Education (AIED 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13916))

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Abstract

Artificial Intelligence is becoming pervasive in higher education. While these tools can provide customized intervention and feedback, they may cause ethical risks and sociotechnical implications. Current ethical discussions often focus on established technical issues and overlook further implications from students’ perspectives, which may increase their vulnerability. Taking a student-centered view, we apply the story completion method to understand students’ concerns about the future adoption of various analytics-based AI tools. 71 students elaborated on the provided story stems. A qualitative analysis of their stories reveals students’ perceptions that AIEd may disrupt aspects of the pedagogical landscape such as learner autonomy, learning environments and approaches, interactions and relationships, and pedagogical roles. This study provides an initial insight into student concerns about AIEd and a foundation for future research.

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Notes

  1. 1.

    Our study design sees the future implementation of AIEd techniques, where AIEd would entail AI, LA (Learning Analytics), and EDM (Educational Data mining techniques) tools. Therefore, we may use these terms interchangeably in the current context.

  2. 2.

    The full scenarios are presented at: https://sites.google.com/view/storystems

References

  1. Nawaz, S., et al.: Moments of confusion in simulation-based learning environments. Journal of Learning Analytics 7(3), 118–137 (2020)

    Article  Google Scholar 

  2. Tzimas, D., Demetriadis, S.: Ethical issues in learning analytics: A review of the field. Education Tech. Research Dev. 69(2), 1101–1133 (2021)

    Article  Google Scholar 

  3. Clark, J.-A., et al.: Critical success factors for implementing learning analytics in higher education: A mixed-method inquiry. Australasian Journal of Educational Technology (2020)

    Google Scholar 

  4. Tsai, Y.-S., Gasevic, D.: Learning analytics in higher education---challenges and policies: a review of eight learning analytics policies. In: Proceedings of 7th LAK conference (2017)

    Google Scholar 

  5. Han, B., Buchanan, G., Mckay, D.: Learning in the Panopticon: Examining the Potential Impacts of AI Monitoring on Students. In: Proceedings of the 34th OzCHI Confernce (2023)

    Google Scholar 

  6. Dunne, A., Raby, F.: Speculative everything: design, fiction, and social dreaming (2013)

    Google Scholar 

  7. Walker, D., Myrick, F.: Grounded theory: An exploration of process and procedure. Qual. Health Res. 16(4), 547–559 (2006)

    Article  Google Scholar 

  8. Schumacher, C., Ifenthaler, D.: Features students really expect from learning analytics. Comput. Hum. Behav. 78, 397–407 (2018)

    Article  Google Scholar 

  9. Southgate, E., et al.: Artificial intelligence and emerging technologies in schools (2019)

    Google Scholar 

  10. Nawaz, S., et al.: Analysis of task difficulty sequences in a simulation-based POE environment. In: Artificial Intelligence in Education. Ifrane, Morocco (2020)

    Google Scholar 

  11. Piaget, J.: The development of thought: Equilibration of cognitive structures. Viking (1977)

    Google Scholar 

  12. Van Der Graaf, J., et al.: Do instrumentation tools capture self-regulated learning?. In: International learning analytics and knowledge (LAK) conference (2021)

    Google Scholar 

  13. Whitelock-Wainwright, A., et al.: An exploratory latent class analysis of student expectations towards learning analytics services. The Internet and Higher Education, p. 100818 (2021)

    Google Scholar 

  14. Boud, D., Cohen, R.: Peer learning in higher education: Learning from and with each other. Routledge (2014)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computing and Information Systems, University of Melbourne, Parkville, Australia

    Bingyi Han & George Buchanan

  2. Melbourne Assessment, MGSE, University of Melbourne, Parkville, Australia

    Sadia Nawaz

  3. Faculty of IT, Monash University, Clayton, Australia

    Sadia Nawaz

  4. School of Computing Technologies, RMIT University, Melbourne, Australia

    Dana McKay

Authors
  1. Bingyi Han
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  2. Sadia Nawaz
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  3. George Buchanan
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  4. Dana McKay
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Corresponding author

Correspondence to Bingyi Han .

Editor information

Editors and Affiliations

  1. University of Southern California, Los Angeles, CA, USA

    Ning Wang

  2. University of British Columbia, Vancouver, BC, Canada

    Genaro Rebolledo-Mendez

  3. North Carolina State University, Raleigh, NC, USA

    Noboru Matsuda

  4. Despacho 3.01, UNED-Grupo de InvestigaciĂłn aDeNu, Madrid, Spain

    Olga C. Santos

  5. University of Leeds, Leeds, UK

    Vania Dimitrova

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Han, B., Nawaz, S., Buchanan, G., McKay, D. (2023). Ethical and Pedagogical Impacts of AI in Education. In: Wang, N., Rebolledo-Mendez, G., Matsuda, N., Santos, O.C., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2023. Lecture Notes in Computer Science(), vol 13916. Springer, Cham. https://doi.org/10.1007/978-3-031-36272-9_54

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  • DOI: https://doi.org/10.1007/978-3-031-36272-9_54

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-36271-2

  • Online ISBN: 978-3-031-36272-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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