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|volume=Vol-3696
|authors=Mohammed Saqr,Sonsoles López-Pernas,Miguel Ángel Conde,Olga Pavlović,Miroslava Raspopović Milić
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https://ceur-ws.org/Vol-3696/preface.pdf
The Critical Challenges of Artificial Intelligence in Education
Mohammed Saqr1, Sonsoles López-Pernas1, Miguel Á. Conde2,3, Olga Pavlovic4, Miroslava
Raspopovic Milic4
1 University of Eastern Finland, School of Computing, Yliopistokatu 2, 80100 Joensuu, Finland
2 University of León, Escuela de Ingenierías – Campus de Vegazana S/N, León, 24071, Spain
3 Universidad de Salamanca, Escuela Politécnica Superior de Zamora – Av. de Requejo, 34, 49029 Zamora, Spain
4 Belgrade Metropolitan University, Faculty of Information Technology, Tadeuša Košćuška 63, 11000 Belgrade, Serbia
1
1. Introduction
Throughout history, technology has played a transformative role in shaping societies, reframing
economies, and changing the way we produce and consume knowledge. Waves of technological
advancement have always been associated with ripples of hope and societal aspirations as well as
anxiety and cautious anticipation of the disruption. The recent breakthrough in generative artificial
intelligence (AI) capabilities is another example, yet with a breathtaking speed [1]. The accelerated
pace with which generative AI has evolved has broadened these fears among many [2, 3]. In tandem
with the pace, generative AI is also spreading to novel areas and applications almost every day [4].
Whereas technology and innovation are facts of life, the disruption has always affected manual and
low-skilled jobs. This is not the case with AI. The latest generative AI has proven to replicate high
cognitive functions that were long believed to be an exclusive preserve of highly educated humans. In
layman's terms, there is a new player in town and the word is that the new player will take over the
whole town [5].
Whether it is hype or reality, it is too early to know. Nevertheless, the fear of AI dominance may be
justified by several credible examples in the past where automation has been both transformational and
disruptive across a wide range of social and industrial domains. Lessons learned from automation tell
us that AI can, on the one hand, contribute to the rejuvenation of economies and the creation of new
jobs that capitalize on AI products and services. On the other hand, AI may —and most probably will—
change professions and reshape our knowledge-based economy. Therefore, education has to respond to
these new realities if they may arise [5]. If the past has told us anything, it is that transformations did
not happen overnight or result in sudden mass unemployment, but rather allowed for a gradual transition
from reliance on agriculture to industrialization. Such gradual transformation has helped societies to
adapt, and in many cases, has left the old side-by-side with the modern. The recent wave of AI —though
fast-paced— should not be different [6].
Among the critical challenges we face with AI is to manage a transition where we prepare the next
generation of students to meet the job market demands, modernize our curricula, and promote diversity
and inclusion. A transition where benefits are shared across society at large to avoid the "paradox of
plenty," in which society is rich in aggregate but many are left behind in need [5]. The challenges with
AI extend our evaluative judgment of AI and the byproducts thereof. It is no secret that AI has been
plagued by perpetual cycles of overhype. Over the past decades, AI has gone through multiple cycles
of hype, subsequent disillusionment, criticism, and a reduction of funding for research and industry.
Therefore, a critical realistic approach is both necessary and essential. Several researchers are therefore
calling for critical AI literacy to be an essential part of our education besides AI skills [7, 8].
Proceedings for the 14th International Conference on e-Learning 2023, September 28-29, 2023, Belgrade, Serbia
EMAIL: mohammed.saqr@uef.fi (A. 1); sonsoles.lopez@uef.fi (A. 2); mcong@unileon.es (A. 3); olga.mijailovic@metropolitan.ac.rs (A. 4);
miroslava.raspopovic@metropolitan.ac.rs (A. 5)
ORCID: 0000-0001-5881-3109 (A. 1); 0000-0002-9621-1392 (A. 2); 0000-0001-5881-7775 (A. 3); / (A. 4); 0000-0003-2158-8707 (A. 5)
©️ 2023 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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Furthermore, the world is messy, chaotic, and far from perfect and so is the data that has been fed
into AI models [9]. The rush to embed AI into real-life applications without proper guardrails or audits
has resulted in catastrophic results that affected the most vulnerable factions of society. A large strain
of such examples extend from education to tax evaluation. Unfortunately, these problems continue to
appear on a daily basis. Take for example Google Gemini which was accused of racism just last month
and the long strands of examples of gender, racial, and political bias that flood social media from other
generative AI models. It is inconceivable to think that any benefits can outweigh the possible harms
that are possible if AI is abused or misused if it falls into the wrong hands given what we have
experienced when it is in the “right hands” [10].
Reliance on AI will probably depend on sanitizing AI and making AI byproducts safe, accurate,
consistent, and devoid of bias. So far, the progress in AI has been evolving hand in hand with cracks
and loopholes appearing in AI models. Only the future will tell if we will be able to harness the
transformative power of AI or live with an unrestrained monster. Education has to be prepared for either
situation by making critical AI literacy a central component of our curricula. Critical AI literacy entails
being able to evaluate and question AI systems and tools, as well as their safety and judgment. This
requires students to understand, not only how to make use of AI tools, but also —to some extent— the
underlying mechanisms of how AI works to be aware of potential biases and unfair situations, as well
as the limitations of what AI can do. Only then, our students will be prepared for whatever the future
will bring.
In conclusion, the transformative potential of AI in education is undeniable. However, we can only
realize such aspirations if we approach AI with a critical lens. To do so, we must equip students with
critical AI literacy to empower them to navigate the complexities and ensure that AI works for the
welfare of humanity. This critical literacy will not only provide students with the skills needed to
succeed in the future job market but also help build a generation of responsible AI users who can
leverage this technology for the greater good and avoid its drawbacks.
2. References
[1] F. J. García-Peñalvo, "Generative Artificial Intelligence in Higher Education: A 360°
Perspective," presented at the IFE Conference 2024, Monterrey, Mexico, 2024. [Online].
Available: https://doi.org/10.5281/zenodo.10499828.
[2] W. M. Lim, A. Gunasekara, J. L. Pallant, J. I. Pallant, and E. Pechenkina, "Generative AI and
the future of education: Ragnarök or reformation? A paradoxical perspective from
management educators," The International Journal of Management Education, vol. 21, no. 2,
p. 100790, 2023/07/01/ 2023, doi: https://doi.org/10.1016/j.ijme.2023.100790.
[3] F. J. García-Peñalvo, "La percepción de la Inteligencia Artificial en contextos educativos tras
el lanzamiento de ChatGPT: disrupción o pánico," Education in the Knowledge Society
(EKS), vol. 24, p. e31279, 02/06 2023, doi: 10.14201/eks.31279.
[4] S. Schöbel, A. Schmitt, D. Benner, M. Saqr, A. Janson, and J. M. Leimeister, "Charting the
Evolution and Future of Conversational Agents: A Research Agenda Along Five Waves and
New Frontiers," Information Systems Frontiers, vol. 26, no. 2, pp. 729-754, 2024/04/01 2024,
doi: 10.1007/s10796-023-10375-9.
[5] M. Cazzaniga et al., "Gen-AI: Artificial Intelligence and the Future of Work," International
Monetary Fund, Washington, DC. , 2024. [Online]. Available: https://www.imf.org/-
/media/Files/Publications/SDN/2024/English/SDNEA2024001.ashx
[6] D. Acemoglu and P. Restrepo, "Artificial Intelligence, Automation and Work," National
Bureau of Economic Research Working Paper Series, vol. No. 24196, 2018, doi:
10.3386/w24196.
[7] M. Saqr, "Big data and the emerging ethical challenges," (in eng), International Journal of
Health Sciences (Qassim), vol. 11, no. 4, pp. 1-2, Sep-Oct 2017.
[8] V. C. Müller, "Ethics of Artificial Intelligence and Robotics," in The Stanford Encyclopedia
of Philosophy E. N. Z. a. U. Nodelman Ed., Fall 2023 ed., 2023.
[9] I. Kilanioti, M. Saqr, and M. Á. Queiruga-Dios, "Editorial: Diversity in the social sciences:
researching digital education in and for the global south," (in English), Frontiers in
Education, Editorial vol. 9, 2024-April-02 2024, doi: 10.3389/feduc.2024.1392017.
[10] X. Ferrer, T. v. Nuenen, J. M. Such, M. Coté, and N. Criado, "Bias and Discrimination in AI:
A Cross-Disciplinary Perspective," IEEE Technology and Society Magazine, vol. 40, no. 2,
pp. 72-80, 2021, doi: 10.1109/MTS.2021.3056293.