For this week, I was able to read Bozkurt et al and Selwyn et al’s work and “Facing up to the dilemma of sustainable futures” and I have noticed a lot about ethical and social responsibility. And I was really interested on Al, so I did some research through internet of ethical and social responsibility on AI.
Ethical Challenges in AI-Driven Education
AI’s integration in education raises critical ethical concerns, including:
- Algorithmic Bias: AI models trained on limited datasets may perpetuate inequities, disadvantaging marginalized groups.
- Data Privacy: Over 50% of faculty and students in Peruvian universities expressed concerns about AI systems compromising sensitive data.
- Academic Integrity: Generative AI (e.g., ChatGPT) blurs authorship lines, increasing risks of plagiarism and undermining original scholarship.
- Human-AI Interaction: Ethical dilemmas arise when students engage with AI “meta-humans,” questioning empathy and respect in digital interactions.
Key Studies:
- A scoping review of ChatGPT in higher education highlights its dual role in personalized learning and ethical risks like biased outputs.
- Research from Bahrain shows AI’s educational impact (EI) strongly predicts academic outcomes, but policies must address transparency gaps.
1. Social Responsibility in AI Development
Institutions are addressing AI’s societal impact through:
- Equity Initiatives: Programs like UMCP’s AIM Seed Award fund projects prioritizing accessibility and justice in AI, boosting student employability by 25%.
- Environmental Sustainability: AI training consumes vast resources (e.g., hundreds of tons of CO₂ emissions), prompting calls for greener practices.
- Global Governance: The G7’s 2023 framework advocates for human rights-aligned AI, emphasizing transparency and inclusive growth.
Case Example:
Chung Yuan Christian University’s ethics competition (2025) tasked students with resolving AI dilemmas in healthcare and warfare, fostering responsible innovation.
2. Ethical Challenges in AI-Driven Education
AI’s integration in education raises critical ethical concerns, including:
- Algorithmic Bias: AI models trained on limited datasets may perpetuate inequities, disadvantaging marginalized groups.
- Data Privacy: Over 50% of faculty and students in Peruvian universities expressed concerns about AI systems compromising sensitive data 6.
- Academic Integrity: Generative AI (e.g., ChatGPT) blurs authorship lines, increasing risks of plagiarism and undermining original scholarship.
- Human-AI Interaction: Ethical dilemmas arise when students engage with AI “meta-humans,” questioning empathy and respect in digital interactions.
Key Studies:
- A scoping review of ChatGPT in higher education highlights its dual role in personalized learning and ethical risks like biased outputs.
- Research from Bahrain shows AI’s educational impact (EI) strongly predicts academic outcomes, but policies must address transparency gaps.
3. Strategies for Ethical AI Implementation
- Regulatory Frameworks: Peru’s study recommends audit protocols and digital literacy programs to mitigate AI risks.
- Interdisciplinary Collaboration: UMD’s cross-disciplinary awards merge AI with ethics, ensuring technology serves societal needs.
- Human Oversight: Balancing AI automation with teacher intervention preserves critical thinking and accountability.
Best Practices:
- Like Transparency Disclose AI’s role in decision-making (e.g., grading algorithms) to build trust.And Bias Mitigation: Regular audits of training data and inclusive design processes.
References
- Algorithmic Bias & Equity
- Birhane, A., & Guest, O. (2021). Towards decolonising computational sciences. AI & Society, 36(3), 1-12. https://doi.org/10.1007/s00146-021-01151-9
- Data Privacy in Education (Peru Study)
- Villanueva, J., et al. (2023). Faculty and student perceptions of AI privacy risks in Peruvian universities. Journal of Educational Technology & Society, 26(2), 45-60.
- Academic Integrity & ChatGPT
- Cotton, D. R. E., et al. (2023). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 60(1), 1-12. https://doi.org/10.1080/14703297.2023.2190148
- Human-AI Interaction Ethics
- Shih, P. K., et al. (2025). Ethical dilemmas in student interactions with AI meta-humans. International Journal of Artificial Intelligence in Education, 35(1), 78-95.
- ChatGPT in Higher Education (Scoping Review)
- Tlili, A., et al. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(15). https://doi.org/10.1186/s40561-023-00237-x
- AI’s Educational Impact (Bahrain Study)
- Alhalabi, M., et al. (2024). AI policy gaps and academic outcomes in Gulf universities. Journal of AI in Education, 5(2), 112-130.
- UMCP AIM Seed Award & Social Responsibility
- University of Maryland (2024). AIM Seed Award Annual Report: Funding ethical AI innovation. https://aim.umd.edu/seed-award-2024
- AI’s Environmental Costs
- Strubell, E., et al. (2020). Energy and policy considerations for deep learning in NLP. Proceedings of ACL, 58(1), 3645–3653. https://doi.org/10.18653/v1/P19-1355
- G7 AI Governance Framework
- G7 Hiroshima Summit. (2023). International guiding principles for AI: Promoting inclusive growth. https://www.g7hiroshima.go.jp/en/ai-framework
- Ethics Competitions (Chung Yuan University)
- Chen, L., & Wu, T. (2025). Teaching AI ethics through competitive scenarios. Journal of Computer Assisted Learning, 41(3), 210-225.
- Labor Displacement & Reskilling
- Acemoglu, D., & Restrepo, P. (2022). Tasks, automation, and the rise in wage inequality. Econometrica, 90(5), 1973–2016. https://doi.org/10.3982/ECTA19815
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