Authors : Micah Okpara; Blessing Chimezie Uzo; Ebere Nwanneka Chukwuemeka; Ponsak Stephen Bande; Fortune Nkem Okpara

Volume/Issue : Volume 11 - 2026, Issue 9 - September

Google Scholar : https://tinyurl.com/2svt3hmd

DOI : https://doi.org/10.38124/ijisrt/26sep959

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Generative artificial intelligence (AI) applications such as ChatGPT present a distinct challenge for computer science (CS) students. While they offer individualized learning support, unregulated use risks facilitating academic shortcuts and eroding critical thinking skills. This article introduces Lion-AI, a chatbot tutor developed to augment the CS educational experience. Lion-AI integrates retrieval-augmented generation (RAG), rooted in university-specific course materials, with an engineered Socratic interaction policy. Unlike general-use tools, Lion-AI is designed to classify student requests, redirect direct-solution requests through diagnostic questioning, and provide guidance through one-question-at a-time interaction. The study describes a method for creating ethically constrained AI tutors in CS education. It presents the system’s three-tier architecture, the RAG pipeline, and the transparent interaction policy behind the tutor, positioning Lion-AI as a deployable proof of concept for combining curriculum grounding with explicit Socratic constraints. A controlled classroom study is planned as the next step.

Keywords : Artificial Intelligence in Education (AIEd); Large Language Models (LLMs); Retrieval-Augmented Generation (RAG); Prompt Engineering; Computer Science Education; Socratic Method.

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