Part II
By: Rasha Halat and Lina Khair Rahme
June 30, 2024
This is Part 2 in a 2-part series. Read Part 1 here.
As we delve deeper into the discussion on AI’s role in education, it becomes crucial to address the challenges and ethical considerations that emerge. While the first part of this article highlighted the transformative potential of AI, this part will focus on the shadows of inequity looming on the AI horizon and the steps we can take to navigate these challenges responsibly.
The embrace of AI in education offers a multitude of benefits, painting a future filled with promise. However, as with any powerful tool, there are inherent challenges and concerns that we must be wary of. It is essential to recognize these potential pitfalls not as deterrents, but as areas needing careful consideration to truly realize AI’s transformative potential.
Biased Data, Biased Outcomes
While AI systems are often perceived as neutral entities, they are inherently influenced by the data they are trained on. Data bias can reflect prejudice against a certain race, culture, or age (Kaminshka, 2022). For example, consider an AI tool designed to assess students’ essays. If this tool has been predominantly trained on literature from Western cultures, it might inadvertently undervalue or misinterpret content rooted in African or Middle Eastern discourse styles. Such biases, even if unintentional, can perpetuate stereotypes and reinforce societal prejudices, especially if not checked and balanced.
The Digital Divide: Widening the Gap
The rapid technological advancement might seem like an equalizer, but there is a lurking concern: the digital divide. Let us take two schools – one private school in Dubai with state-of-the-art infrastructure and another in a rural refugee school in Lebanon, with limited access to resources. While students in the former have the luxury of advanced AI tools to enhance their learning, those in the latter might not even have basic digital access. Without global efforts to bridge this divide, AI risks exacerbating educational disparities rather than mitigating them.
Over-Reliance: The Potential for Dependency
As AI tools become more integrated into the learning process, there is a possible danger of over-reliance on them by both students and teachers. Students from under-resourced schools or disadvantaged backgrounds, who might not have the same access to a diverse range of learning experiences, could become overly dependent on AI. For instance, if students constantly turn to an AI tutor for answers instead of being trained on problem-solving or critical thinking, they might fail to develop these crucial skills that are essential for academic and life success. Similarly, teachers who heavily depend on AI for instruction and feedback might not engage students in deeper, more personalized learning experiences. This over-reliance can exacerbate educational inequities. The challenge is ensuring that AI serves as a supportive tool that complements traditional teaching methods and fosters an equitable learning environment rather than becoming a crutch.
The Emotional Quotient: Beyond Algorithms
It is a student’s first presentation, and they’re battling nerves. Post-presentation, an AI might give them feedback based on metrics like clarity, content, and structure. But what about the gentle encouragement a human teacher offers? Based on one study, students perceived feedback generated by GPT as insensitive (Nagelhout, 2023). The pat on the back, the nod of assurance, emotions, empathy, and interpersonal connections, which play a pivotal role in holistic learning, are realms that algorithms have yet to conquer.
Moreover, generatuve AI technologies might pose problems for the development of students’ social-emotional skills (Prothero, 2023). How can children learn these skills? How would that affect their relationships? And how can they navigate online environments full of AI-generated disinformation especially since many are turning to AI platforms for even personal advice? This is one of the areas of real concern for many educators (Prothero, 2023).
Looking Forward: A Roadmap to Embracing AI’s True Potential
While the challenges of AI in education are real, they are not insurmountable. With thoughtful planning, collaboration, and a keen understanding of both the technology and the diverse needs of learners, we can plan the path forward. This roadmap provides actionable guidelines to ensure that as we embrace the power of AI, we do so with equity, fairness, and the holistic development of every student at the heart of our practices.
This roadmap provides actionable guidelines to ensure that as we embrace the power of AI, we do so with equity, fairness, and the holistic development of every student at the heart of our practices.
Cultivating Diverse and Representative Data Sets
The biases in AI can be traced back to the data they are trained on. As such, it is crucial to ensure that these datasets are diverse and representative of various cultures, traditions, and perspectives. Institutions should collaborate with diverse groups, including experts from marginalized communities, to review, refine, and augment training data. Moreover, periodic audits of AI-driven tools can help identify and rectify biases that might seep in over time.
Bridging the Digital Divide with Global Partnerships
Addressing the digital divide requires a collective global effort. Governments, NGOs, and private enterprises can form partnerships to provide necessary infrastructure in underserved areas. Initiatives like One Laptop per Child or Google’s Project Loon, which aimed to provide internet access via balloons, are steps in the right direction. Schools and colleges can also collaborate in ‘sister school’ programs, where resource-rich institutions assist and share resources with those in less privileged regions.
Training Educators for the AI Age
While AI tools become more commonplace, educators remain central to the learning experience. Regular training programs should be instituted, ensuring that teachers are not just familiar with AI but can also critically assess its role in their classrooms. They should be equipped to identify potential pitfalls and intervene when necessary. The synergy between a well-informed educator and AI can offer the best outcomes for students.
The synergy between a well-informed educator and AI can offer the best outcomes for students.
Establishing a Balanced Dependency
To avoid over-reliance on AI, educators and the curricula should emphasize critical thinking, problem-solving, and independent research skills. Guidelines can be established where AI tools serve as secondary resources rather than primary go-to solutions. For instance, during problem-solving sessions, students could be encouraged to attempt solutions manually before consulting AI-driven platforms. Another option is using tools like Socratic , which offers step-by-step explanations and resources to guide students towards the solution, promoting independent problem-solving skills while providing a digital safety net. This ensures students exercise their cognitive abilities while still having AI as a safety net.
Continuous Dialogue and Feedback Mechanisms
A feedback loop involving educators, students, parents, and tech developers is essential. Regular discussions can help gauge the effectiveness of AI tools, gather feedback, and adjust strategies accordingly. For example, if a particular AI-driven module consistently misinterprets a concept, feedback from students and educators can lead to timely rectifications.
Conclusion: The Future of AI–A Delicate Balance
As we step into the AI-driven educational revolution, the path ahead is both promising and fraught with challenges. The allure of personalized learning, adaptive platforms, and instant access to knowledge is undeniable. Yet, the shadows of bias, inequity, and potential over-reliance remind us that this tool, as powerful as it might be, is not a quick cure all.
However, the answer is not to reject or abandon this technological marvel. It’s about striking the right balance. By simultaneously approaching AI with both enthusiasm and caution, we can benefit from its immense potential while sidestepping its pitfalls. As educators, policymakers, tech developers, and most importantly, lifelong learners, our mission is clear: not to shun or blindly embrace AI, but to mold, refine, and employ it as a force for good.
In this journey, let us remember that AI in education, at its heart, is a tool – its efficacy, its impact, and its legacy will be dictated by how we, the human stakeholders, choose to utilize it. As we move forward, let us do so with open dialogue, collaboration, and a shared vision of an inclusive, equitable, and enlightened educational landscape for all.
References
Kamińska, J. (2022, May 18). 8 Types of Data Bias that Can Wreck Your Machine Learning Models. Statice. Retrieved from https://www.statice.ai/post/data-bias-types#:~:text=Biased%20datasets%20don%27t%20accurately,%2C%20culture%2C%20or%20sexual%20orientation.
Nagelhout, R. (2023, November 17). Better Feedback With AI. Harvard Graduate School of Education. Retrieved from https://www.gse.harvard.edu/ideas/usable-knowledge/23/11/better-feedback-ai
Prothero, A. (2023, November 13). Artificial Intelligence and Social-Emotional Learning Are on a Collision Course. Education Week. Retrieved from https://www.edweek.org/leadership/artificial-intelligence-and-social-emotional-learning-are-on-a-collision-course/2023/11