The future is now
This post takes inspiration from “From Cautious Optimism to Ambivalence,” by Nitin Madnani, Whitney Coggeshall, Katerina Schenke, Rahul Divekar, and Kenneth Steimel.
Artificial intelligence (AI) is no longer a concept of the future: it is here, and it is reshaping industries around the world.
In fact, a recent Forbes Magazine survey revealed that over half of all business owners use AI in some capacity.
And the field of education is certainly no exception. Innovative pedagogy now involves AI tools capable of enhancing teaching and learning in the name of a more personalized and efficient educational experience. However, public attitudes toward AI and education are still in flux, shifting noticeably towards ambivalence since the popularization of generative AI models like ChatGPT®. According to Donna St. George and Susan Svrluga of The Washington Post, “some public schools, including those in New York City and Los Angeles, have blocked access to [these kinds of AI tools], citing concerns about student learning and cheating.”
Technological adaptations for a changing world
AI has a multifaceted impact on education.
Consider how “educators are also using it to help develop tests, generate case studies, write emails, and rethink teaching strategies” so that they better reflect our changing world. For example, there is the case of Intelligent Tutoring Systems (ITS). These AI-powered educational systems adapt to the individual learning pace of each student to supply customized feedback and tailored exercises. With ITS, students can learn at their own speed, while teachers can watch progress and differentiate their teaching methods.
In addition, AI automates and enhances content production, which has inherent value to education. For example, after analyzing student data, AI platforms can create personalized and engaging educational material, including interactive quizzes and simulations. Furthermore, as Stéphan Vincent-Lancrin notes in the New England Journal of Public Policy, “smart technologies give students with impairments and special needs access to curriculum materials and allow those students to participate in learning activities to an extent that was not possible before, here again increasing the effectiveness of education.”
Advances in AI-driven predictive modeling now allow educators to forecast outcomes such as student performance. Such data-driven analysis can, in theory, improve individual student performance – teachers can now adjust their instructional approach to meet or avoid a forecasted outcome. However, questions of AI bias and accuracy do prevail in this realm. When a team of Education Next researchers tested how predictive modeling foretold college outcomes for students, they found instances of biases, particularly concerning students from underrepresented backgrounds. The takeaway? Improving reliability and transparency in these models is essential to increase public trust and acceptance of AI systems.
Taking a clear-eyed approach to innovation
Integrating AI into education requires careful consideration of potential advantages and disadvantages.
To that end, it is important to remember that despite the many benefits AI offers, it cannot replace human interaction and empathy, both of which are crucial elements in teaching and learning. Interestingly enough, some organizations have begun trying to program empathy into generative AI models, although many scholars see limitations. For instance, Pragya Agarwal at Wired Magazine noted that “emotional AI algorithms, even when trained on large and diverse data sets, reduce facial and tonal expressions to an emotion without considering the social and cultural context of the person and the situation.”
Educators must strive to embrace how this technology makes pedagogy more innovative and engaging for students, while never losing sight of the evolving public attitudes and challenges that AI-enabled products pose.
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