AI integration in universities: Fostering personalised learning amidst ethical debates

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A computer science student can attain code line-by-line, without having to comprehend the fundamentals of programming. Chemistry undergraduates who struggle with complex organic curly arrows’ mechanisms in chemical reactions can ask for simpler explanations at 3am.

This highlights a significant change towards personalised learning, abating the physical T&L support that once could be only delivered in the lecture class. In this regard, AI tools function as an infinitely knowledgeable companion, capable of boosting comprehension and fostering independent learning for students by filling the gaps within the lectures and textbooks.

In addition, the AI tools also offer support to empower students to overcome syllabus conceptual limitations at their own paces.

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Ethical dilemma

However, AI tools also create a set of ethical issues that blur the line between learning and cheating. The same tool that can explain a complex topic, can also be applied to complete a student's assignment.

This makes it almost impossible to determine the student's intention. It is uncertain whether using AI tools to generate an essay outline is a genuine study aid or a form of plagiarism.

Similarly, having AI tools to walk through the steps of a mathematical problem could be seen as efficient learning or as academic dishonesty.

Accordingly, the lack of a clear boundary forces instructors to constantly doubt the authenticity of student work and struggle to keep pace with new software that is designed to disguise AI assistance.

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Consequently, the foundation of undergraduate education is being stress-tested. Student’s assessments that are designed for an era of essay writings and copy-pasted texts are woefully inadequate for the age of generative AI.

Conversely, a profound curricular transformation is required and beginning to take place.

Indeed, the lecturers have been actively moving away from the traditional after-class essay-based assignment, and start adopting in-situ writing assignments, oral presentations, and project-based learning, as well as problem-based learning that emphasises real understanding over an elegant final product.

Furthermore, the main challenge encountered by universities is not just pushing the student’s grades or results, but about proactively improving T&L mode. The aim must harness the AI’s potential as transformative T&L tools, while educating students to develop critical thinking in making ethical decisions to utilize the AI tools responsibly.

The future of university education should not ban or limit AI advancements, but it should be integrated in a way that can foster true understanding, establishing the core value of the T&L process in adopting AI tools.