Abstract:

Teaching research methodology in Computer Science programs remains a significant challenge, particularly when students must develop the ability to analyze scientific literature, identify knowledge gaps, and construct conceptual research models. Although the Systematic Literature Review (SLR) method is widely recognized as an effective approach for developing evidence-based research proposals, many undergraduate students struggle with the complexity of the process and the difficulty of identifying relevant academic sources.

To address this challenge, this study proposes an asynchronous online course model supported by Artificial Intelligence tools, designed to guide students step by step in conducting a Systematic Literature Review. The model integrates a structured research methodology based on the SLR approach, through which students learn how to search, analyze, synthesize, and visualize scientific knowledge. The course includes learning modules, methodological guidelines, research templates, and AI agents that guide students in the use of different research tools. In addition, interactive resources such as micro-videos support the progressive development of research competencies within a flexible self-paced learning environment.

The learning process is also supported by several AI-assisted research platforms, including Microsoft Copilot, Elicit, Perplexity, and other tools, which allow students to explore academic literature, analyze scientific articles, and identify patterns and knowledge gaps relevant to the development of evidence-based research proposals. In addition, one module of the course incorporates an AI agent developed in Microsoft Copilot Studio called “Lion Gaming – Assistant Academic Research”, which guides students step by step throughout the research process. The learning experience is structured through a five-phase pedagogical framework called the Lion Gaming Academic Researcher Assistant Intelligence Framework, which includes Exploration Intelligence, Discovery Intelligence, Validation Intelligence, Construction Intelligence, and Publication Intelligence.

This study is part of the research agenda of the Lion Gaming AI Hub 2030 Ecosystem, which promotes the integration of artificial intelligence into education and research. One of its key initiatives, the Lion Gaming Academic Researcher Assistant, supports students and faculty in conducting systematic literature reviews.

Preliminary results suggest that the proposed model improves students’ research competencies, particularly in the critical analysis of scientific literature, identification of knowledge gaps, and development of conceptual research models. The pilot implementation of the asynchronous online course is being carried out in several educational contexts, including students participating in the Lion Gaming Project, courses related to Artificial Intelligence and programming languages (with approximately twenty-thirty students participating in the pilot.), and a research group called the AI Discovery Research Team.

In conclusion, the main contribution of this study is the design and pilot implementation of an AI-assisted asynchronous online course model for teaching Systematic Literature Reviews in Computer Science education, providing a scalable pedagogical framework for developing research competencies among undergraduate students while promoting the responsible use of artificial intelligence in academic research environments.

Keywords:

AI agents, AI-Assisted Learning, Artificial Intelligence in Education, Systematic Literature Review (SLR), AI in Education, AI tools.