Embracing Generative AI in Education: Navigating Opportunities and Risks

Embracing Generative AI in Education: Navigating Opportunities and Risks

By Marvin Starominski-Uehara, January 10th, 2025

For the past year and a half, I have fully embraced artificial intelligence (AI) as a tool to assist both learning and teaching. During this time, I have witnessed firsthand not only the perils but also the immense opportunities this breakthrough technology offers. Contrary to expectations, the rise of Generative AI -- or large language models -- has not universally enhanced (Mollick 2023) critical thinking skills among my students. Instead, it has dramatically widened the gap in creativity and originality. This troubling trend, however, should not be interpreted as a condemnation of AI’s role in education. Rather, it presents an opportunity to reevaluate current practices and ensure that all stakeholders (administrators, faculty, parents, and students) collaborate to recalibrate the aggregated learning curve. The goal is to extend the benefits of AI, currently enjoyed by a select few students who use it effectively, to all learners through (i) personalized guidance; (ii) open and free access; and (iii) structured support.

(The percentages cited in this article are drawn from an anonymous online poll of sixty students across two online courses I taught during the Fall 2024 semester. See table below)

Context: Seventy-two percent of my students believe AI accelerates their learning, while 54% worry it may undermine their originality. This duality highlights a critical challenge: leveraging AI’s potential while preserving educational integrity and depth. In this article, I propose initial strategies to address this dilemma by addressing the unique concerns of stakeholders, fostering a framework that balances innovation with accountability.

For Administrators: Prioritizing Integrity and Equity: Administrators rightly focus on safeguarding academic integrity. The poll reveals that 51% of my students fear falling behind peers who use AI, a concern that risks incentivizing misuse. Many institutions have opted to combat low-quality AI-generated work by upgrading detection tools, hoping to deter malpractice and uphold traditional educational standards. However, scholars caution that (i) overreliance on detection systems could stifle originality (Ardito 2023); (ii) warn that such systems also exhibit concerning discrepancies between false positives and false negatives (Gegg-Harrison & Quarterman 2024); (iii) might not be aligned with what ‘constitutes plagiarism in the digital age’ (Hutson 2024:21); or (iv) exacerbate systemic inequities (Perkins et al. 2024:18). To mitigate this, administrators are hosting workshops on ethical AI use and revising honor codes to reinforce academic excellence. Some are proactively spotlighting success stories (Ouellette 2024) to demonstrate how responsible adoption can speed up personalized learning experiences while enhancing institutional reputation.

For Faculty: Redesigning Pedagogy: Faculty face the challenge of reimagining curricula and content delivery in the AI era. With 64% of my students using AI for reading and 32% for writing, my assignments must evolve. Rather than banning AI, I now design assessments that prioritize critical analysis -- for example, peer reviews of AI-generated content or projects comparing human and machine outputs. The 42% of my students who believe AI hinders cognitive development benefit from exercises dissecting its limitations. By framing AI as a collaborative tool, not a replacement, I cultivate skills no algorithm can replicate.

For Parents: Ensuring Fairness and Creativity: Parents seek reassurance about fairness and creativity. While 65% of my students report using AI in unique ways -- suggesting its potential to inspire innovation --, unequal access to evolving tools and awareness of their limitations has, as I have observed, widened learning gaps exponentially. Schools can address this by (i) demystifying AI models; (ii) subsidizing access to leading tools; and (iii) offering hands-on training sessions. Transparent communication, such as emphasizing that 69% of my students find AI enhances learning, can reassure parents that guided use fosters -- not stifles -- independent thought.

For Students: Balancing Tool and Crutch: Students grapple with using AI as a supplement rather than a crutch. While 63% still prefer Google for learning, many of my students rely on AI for tasks like research (52%) or writing (32%). To curb over-dependence, I argue that institutions should teach 'AI literacy', clarifying when to use AI (e.g., clarification and illustration of key concepts) versus when to think independently (e.g., refining original arguments). Peer mentorship programs, where students share strategies to preserve originality (addressing the 54% anxious about losing their voice), foster accountability.

Conclusion: Collaboration Over Resistance: AI tools, particularly large language models, are neither villains nor saviors -- they are collaborators. By aligning policies with stakeholder needs -- detecting misuse, redesigning assignments, ensuring equity, and promoting mindful use -- we can transform risks into opportunities. The goal is not to resist AI but to empower a generation that wields it wisely, ensuring technology amplifies -- not diminishes -- each student’s potential.

Reference list:

Ardito, C. G. (2023). Contra generative AI detection in higher education assessments. arXiv preprint arXiv:2312.05241.

Gegg-Harrison, W., & Quarterman, C. (2024). AI Detection's High False Positive Rates and the Psychological and Material Impacts on Students. In Academic Integrity in the Age of Artificial Intelligence (pp. 199-219). IGI Global.

Hutson, J. (2024). Rethinking Plagiarism in the Era of Generative AI. Journal of Intelligent Communication, 4(1), 20-31.

Mollick, E. (2023, September 24). Everyone is above average. Retrieved from https://www.oneusefulthing.org/p/everyone-is-above-average 

Ouellette, K. (2024, April 29). MIT faculty, instructors, students experiment with generative AI in teaching and learning. MIT News.

Perkins, M., Roe, J., Vu, B. H., Postma, D., Hickerson, D., McGaughran, J., & Khuat, H. Q. (2024). Simple techniques to bypass GenAI text detectors: implications for inclusive education. International Journal of Educational Technology in Higher Education, 21(1), 53.

keywords: how to use generative AI in education responsibly; impact of AI on student learning and critical thinking; AI literacy skills for students and teachers; how to balance AI tools and academic integrity; practical strategies for using generative AI in school

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