Intellectus: an AI model to enhance student cognitive performance, increase knowledge acquisition, boost academic integrity and mitigate AI over-reliance in education

Conference paper


Razvi, A. and Bashir, E. 2025. Intellectus: an AI model to enhance student cognitive performance, increase knowledge acquisition, boost academic integrity and mitigate AI over-reliance in education. 2025 IEEE International Smart Cities Conference (ISC2). Patras, Greece 06 - 09 Oct 2025 IEEE. https://doi.org/10.1109/isc266238.2025.11293322
TypeConference paper
TitleIntellectus: an AI model to enhance student cognitive performance, increase knowledge acquisition, boost academic integrity and mitigate AI over-reliance in education
AuthorsRazvi, A. and Bashir, E.
Abstract

This study presents Intellectus, a fine-tuned large language model (LLM) designed to enhance student cognitive performance, boost knowledge acquisition and mitigate AI over-reliance in education. In contrast to traditional AI tools that provide direct answers, Intellectus engages learners through Socratic questioning, adaptive scaffolding, and structured prompting. Developed using a no-code Custom GPT approach and grounded in a systematic literature review, Intellectus was evaluated through a triangulated methodology combining student self-assessments, expert ratings, and plagiarism analysis. A/B testing with twelve university students showed significant gains across ten higher-order cognitive skills when using Intellectus compared to ChatGPT, alongside a dramatic reduction in plagiarism rates. Expert evaluations further validated these findings, confirming Intellectus' ability to promote deeper thinking and academic integrity, and T-testing proved statistical significance across domains.

Sustainable Development Goals4 Quality education
Middlesex University ThemeCreativity, Culture & Enterprise
Conference2025 IEEE International Smart Cities Conference (ISC2)
Proceedings Title2025 IEEE International Smart Cities Conference (ISC2)
ISSN
Electronic2687-8860
2687-8852
ISBN
Electronic9798331557737
Paperback9798331557744
PublisherIEEE
Publication dates
Print06 Oct 2025
Online23 Dec 2025
Publication process dates
Accepted2025
Deposited14 Jul 2026
Output statusPublished
Accepted author manuscript
License
File Access Level
Open
Digital Object Identifier (DOI)https://doi.org/10.1109/isc266238.2025.11293322
Web address (URL) of conference proceedingshttps://doi.org/10.1109/ISC266238.2025
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License: CC BY 4.0
File access level: Open

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