AI intelligent tutoring system tailored to the students’ personality and neurodiversity

Over the past few years, several Universities and Educational Institutes have introduced e-learning platforms to support robust alternatives to face-to-face teaching, where students can benefit from them by revisiting topics covered in class without the constraints of time and space. However, despite this considerable flexibility, the role of the instructor as a facilitator is crucial to support learners when they have doubts on their learning or get stuck, by encouraging them to consider suitable strategies to approach the problem, or by providing clarification on some organisational aspects of the module. Providing quality feedback that is tailored to the individual needs of each learner, including personality and neurodiversity, is a challenging task for educators. Developing different methods of learner-specific feedback increases the workload and often fails to fully address learning gaps. The lecturer's empathy, which consists of a deep understanding of students' personal and social situations, care and concern for students' emotions, and compassionate responses, also poses a critical role in student success. Several intelligent tutoring systems have been implemented in e-learning platforms to try to provide immediate feedback to support students, but they focus more on providing feedback on content and often don't tailor feedback with adaptive empathy based on different students' personalities or neurodiversity. In this paper, an AI intelligent tutoring system based on LLM has been implemented within an e-learning platform, fine-tuned to the content and organisational aspects of the final year project module in the IT programme, with the aim of providing immediate feedback based on students’ requests. The software can tailor comments to each student's personality and, where appropriate, neurodiversity, for example, showing genuine interest in responses from introverts or paraphrasing content to improve written comprehension for dyslexics. The neurodiversity information was taken from the user's profile, while personality was extracted using the MBTI (Myers-Briggs Type Indicator). Finally, the software was tested using a bespoke algorithm consisting in a matchmaking process able to detect the level of communication strategies (empathy, creativity, sensitivity) by cross matching the responses received with open online dictionaries to evaluate the effectiveness of the tailored responses.

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