Stakeholder Perspectives on the Ethics of AI in Distance-Based Higher Education
DOI:
https://doi.org/10.19173/irrodl.v24i2.6089Keywords:
artificial intelligence, ethics, distance-based higher education, students, teachers, institutions, theoretical frameworkAbstract
Increasingly, Artificial Intelligence (AI) is having an impact on distance-based higher education, where it is revealing multiple ethical issues. However, to date, there has been limited research addressing the perspectives of key stakeholders about these developments. The study presented in this paper sought to address this gap by investigating the perspectives of three key groups of stakeholders in distance-based higher education: students, teachers, and institutions. Empirical data collected in two workshops and a survey helped identify what concerns these stakeholders had about the ethics of AI in distance-based higher education. A theoretical framework for the ethics of AI in education was used to analyse that data and helped identify what was missing. In this exploratory study, there was no attempt to prioritise issues as more, or less, important. Instead, the value of the study reported in this paper derives from (a) the breadth and detail of the issues that have been identified, and (b) their categorisation in a unifying framework. Together these provide a foundation for future research and may also usefully inform future institutional implementation and practice.
References
Bates, T., Cobo, C., Mariño, O., & Wheeler, S. (2020). Can artificial intelligence transform higher education? International Journal of Educational Technology in Higher Education, 17(1), 42. https://doi.org/10.1186/s41239-020-00218-x
Bell, G., Gould, M., Martin, B., McLennan, A., & O’Brien, E. (2021). Do more data equal more truth? Toward a cybernetic approach to data. Australian Journal of Social Issues, 56(2), 213–222. https://doi.org/10.1002/ajs4.168
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610–623). https://doi.org/10.1145/3442188.3445922
Bidarra, J., Simonsen, H., & Holmes, W. (2020, June). Artificial intelligence in teaching (AIT): A road map for future developments [Presentation]. EMPOWER Webinar Week (EADTU). https://doi.org/10.13140/RG.2.2.25824.51207
Boticario, J. G. (2019, 16–18 October). A roadmap towards personalized learning based on digital technologies and AI at Higher Education. OOFHEC2019: The Online, Open and Flexible Higher Education Conference. Available at https://canal.uned.es/video/5da96278a3eeb0d93f8b4568
Crawford, K., Dobbe, R., Dryer, T., Fried, G., Green, B., Kaziunas, E., Kak, A., Mathur, V., McElroy, E., Sánchez, A. N., Raji, D., Rankin, J. L., Richardson, R., Schultz, J., West, S. M., & Whittaker, M. (2019). AI NOW 2019 report. AI Now Institute. https://ainowinstitute.org/AI_Now_2019_Report.pdf
Dogan, M. E., Goru Dogan, T., & Bozkurt, A. (2023). The use of artificial intelligence (ai) in online learning and distance education processes: A systematic review of empirical studies. Applied Sciences, 13(5), Article 5. https://doi.org/10.3390/app13053056
Godwin-Jones, R. (2022). Partnering with AI: Intelligent writing assistance and instructed language learning. Language Learning and Technology, 26(2), 5–24. https://doi.org/10125/73474
Goel, A. K., & Polepeddi, L. (2017). Jill Watson: A virtual teaching assistant for online education (College of Computing Technical Report No. 503). Georgia Tech. https://smartech.gatech.edu/bitstream/handle/1853/59104/goelpolepeddi-harvardvolume-v7.1.pdf
Hagendorff, T. (2020). The Ethics of AI Ethics: An Evaluation of Guidelines. Minds and Machines, 30(1), 99–120. https://doi.org/10.1007/s11023-020-09517-8
Hashakimana, T., & Habyarimana, J. de D. (2020). The prospects, challenges and ethical aspects of artificial intelligence in education. Journal of Education, 3(7), 14–27. https://stratfordjournals.org/journals/index.php/journal-of-education/article/view/655
Herodotou, C., Rienties, B., Hlosta, M., Boroowa, A., Mangafa, C., & Zdrahal, Z. (2020). The scalable implementation of predictive learning analytics at a distance learning university: Insights from a longitudinal case study. The Internet and Higher Education, 45, 100725. https://doi.org/10.1016/j.iheduc.2020.100725
Holmes, W., & Anastopoulou, S. (2019). What do students at distance universities think about AI? Proceedings of the Sixth ACM Conference on Learning @ Scale. Association for Computing Machinery (Article No.: 45; pp. 1–4). https://doi.org/10.1145/3330430.3333659
Holmes, W., Bektik, D., Whitelock, D., & Woolf, B. P. (2018). Ethics in AIED: Who Cares? (C. Penstein Rosé, R. Martínez-Maldonado, H. U. Hoppe, R. Luckin, M. Mavrikis, K. Porayska-Pomsta, B. McLaren, & B. du Boulay, Eds.; Vol. 10948, pp. 551–553). Springer International Publishing. https://doi.org/10.1007/978-3-319-93846-2
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education. Promises and implications for teaching and learning. Center for Curriculum Redesign.
Holmes, W., Persson, J., Chounta, I.-A., Wasson, B., & Dimitrova, V. (2022). Artificial intelligence and education. A critical view through the lens of human rights, democracy, and the rule of law. Council of Europe. Available at: https://rm.coe.int/artificial-intelligence-and-education-a-critical-view-through-the-lens/1680a886bd
Holmes, W., & Porayska-Pomsta, K. (Eds.). (2023). The ethics of AI in education. Practices, challenges, and debates. Routledge.
Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Buckingham Shum, S., Santos, O. C., Rodrigo, M. M. T., Cukorova, M., Bittencourt, I. I., & Koedinger, K. (2021). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 32, 504–526. https://doi.org/10.1007/s40593-021-00239-1
Holmes, W., & Tuomi, I. (2022). State of the art and practice in AI in education. European Journal of Education: Research, Development and Policies, 57(4), 542–570. https://doi.org/10.1111/ejed.12533
Huang, X. (2021). Aims for cultivating students’ key competencies based on artificial intelligence education in China. Education and Information Technologies, 26, 5127–5147. https://doi.org/10.1007/s10639-021-10530-2
Iniesto, F., Coughlan, T., & Lister, K. (2021). Implementing an accessible conversational user interface: Applying feedback from university students and disability support advisors. Proceedings of the 18th International Web for All Conference. Association for Computing Machinery (Article No.: 45; pp. 1–5) https://doi.org/10.1145/3430263.3452431
Jivet, I., Scheffel, M., Drachsler, H., & Specht, M. (2017). Awareness is not enough: Pitfalls of learning analytics dashboards in the educational practice. In É. Lavoué, H. Drachsler, K. Verbert, J. Broisin & M. Pérez-Sanagustín (Eds.), Data Driven Approaches in Digital Education (pp. 82–96). Springer International Publishing. https://doi.org/10.1007/978-3-319-66610-5_7
Jobin, A., Ienca, M., & Vayena, E. (2019). Artificial intelligence: The global landscape of ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2
Joffe, H. (2012). Thematic analysis. Qualitative research methods in mental health and psychotherapy, 1, 210–223. https://doi.org/10.1002/9781119973249.ch15
Khalil, M., Prinsloo, P., & Slade, S. (2018). User consent in MOOCs: Micro, meso, and macro perspectives. The International Review of Research in Open and Distributed Learning, 19(5). https://doi.org/10.19173/irrodl.v19i5.3908
Kitto, K., & Knight, S. (2019). Practical ethics for building learning analytics. British Journal of Educational Technology, 50(6), 2855–2870. https://doi.org/10.1111/bjet.12868
Knox, J. (2020). Artificial intelligence and education in China. Learning, Media and Technology, 45(3), 298–311. https://doi.org/10.1080/17439884.2020.1754236
Malik, A., Demszky, D., Koh, P. W., Doumbouya, M., Hudson, D. A., Nie, A., Nilforoshan, H., Tamkin, A., Brunskill, E., Goodman, N., & Piech, C. (2021). Education. In R. Bommasani, D. A. Hudson, & E. Adeli (Eds.), On the opportunities and risks of foundation models (pp. 67–72). https://arxiv.org/abs/2108.07258
Miao, F., & Holmes, W. (2021). AI and education: Guidance for policy-makers. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000376709
Nayak, M., & Narayan, K. A. (2019). Strengths and weaknesses of online surveys. Technology, 6(7), https://doi.org/10.9790/0837-2405053138
Nichols, M., & Holmes, W. (2018). Don’t do evil: Implementing artificial intelligence in universities. In J. M. Duart & A. Szűcs (Eds.), Towards personalized guidance and support for learning (pp. 109–117). European Distance and E-Learning Network. https://www.eden-online.org/proc-2485/index.php/PROC/article/view/1669
OpenAI. (2022, November 30). ChatGPT: Optimizing language models for dialogue. OpenAI. https://openai.com/blog/chatgpt/
Ørngreen, R., & Levinsen, K. (2017). Workshops as a research methodology. Electronic Journal of E-Learning, 15(1), 70–81. https://vbn.aau.dk/en/publications/workshops-as-a-research-methodology
Ramesh, D., & Sanampudi, S. K. (2022). An automated essay scoring systems: A systematic literature review. Artificial Intelligence Review, 55(3), 2495–2527. https://doi.org/10.1007/s10462-021-10068-2
Renz, A., & Hilbig, R. (2020). Prerequisites for artificial intelligence in further education: Identification of drivers, barriers, and business models of educational technology companies. International Journal of Educational Technology in Higher Education, 17(1), 14. https://doi.org/10.1186/s41239-020-00193-3
Rets, I., Gillespie, A., & Herodotou, C. (2023). Six Practical Recommendations Enabling Ethical Use of Predictive Learning Analytics in Distance Education. Journal of Learning Analytics, 10(1), (Early Access). https://doi.org/10.18608/jla.2023.7743
Rivera Muñoz, J., Berríos, H., & Arias-Gonzales, J. (2022). Systematic review of adaptive learning technology for learning in higher education. Eurasian Journal of Educational Research, 98, 221–233. https://doi.org/10.14689/ejer.2022.98.014
Slade, S., & Tait, A. (2019). Global guidelines: Ethics in learning analytics. International Council for Open and Distance Education. https://bit.ly/3kKXSvA
Susnjak, T. (2022). ChatGPT: The end of online exam integrity? ArXiv Preprint https://doi.org/10.48550/arXiv.2212.09292
Tahiru, F. (2021). AI in education: A systematic literature review. Journal of Cases on Information Technology, 23(1), 1–20. https://doi.org/10.4018/JCIT.2021010101
Tarran, B. (2018). What can we learn from the Facebook-Cambridge Analytica scandal? Significance, 15(3), 4–5. https://doi.org/10.1111/j.1740-9713.2018.01139.x
Teng, Y., Zhang, J., & Sun, T. (2022). Data-driven decision-making model based on artificial intelligence in higher education system of colleges and universities. Expert Systems, e12820. https://doi.org/10.1111/EXSY.12820
Ubachs, G., Konings, L., & Brown, M. (2017). The envisioning report for empowering universities. EADTU. https://empower-new.eadtu.eu/images/report/The_Envisioning_Report_for_Empowering_Universities_1st_edition_2017.pdf
United Nations Educational, Scientific and Cultural Organization. (2021). Recommendation on the ethics of artificial intelligence. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000381137
United Nations International Children’s Emergency Fund. (2021). Policy guidance on AI for children. UNICEF. https://www.unicef.org/globalinsight/media/2356/file/UNICEF-Global-Insight-policy-guidance-AI-children-2.0-2021.pdf
Uppenbrink, J. (2000). Mendeleyev’s dream. Science, 289(5485), 1696–1696. https://www.jstor.org/stable/i355109
Wagner, G., Lukyanenko, R., & Paré, G. (2022). Artificial intelligence and the conduct of literature reviews. Journal of Information Technology, 37(2), 209–226. https://doi.org/10.1177/02683962211048201
Watters, A. (2021). Teaching machines: The history of personalized learning. MIT Press.
Williamson, B. (2020). Datafication of education. In H. Beetham & R. Sharpe (Eds.), Rethinking pedagogy for a digital age (pp. 212–226). Routledge. https://doi.org/10.4324/9781351252805-14
Wollny, S., Schneider, J., Di Mitri, D., Weidlich, J., Rittberger, M., & Drachsler, H. (2021). Are we there yet? A systematic literature review on chatbots in education. Frontiers in Artificial Intelligence, 4, 654924. https://doi.org/10.3389/frai.2021.654924
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education: Where are the educators? International Journal of Educational Technology in Higher Education, 16(1). https://doi.org/10.1186/s41239-019-0171-0
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International Licence. The copyright of all content published in IRRODL is retained by the authors.
This copyright agreement and use license ensures, among other things, that an article will be as widely distributed as possible and that the article can be included in any scientific and/or scholarly archive.
You are free to
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
The licensor cannot revoke these freedoms as long as you follow the license terms below:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
