Enhancing Computer Science Education through AI-Powered Learning Assistants: An Empirical Investigation of Effectiveness, Student Engagement, and Implementation in Northwestern Nigerian Universities

Abubakar Abba, Muhammad Jumare Haruna

Abstract


The integration of artificial intelligence-powered learning assistants (AIPLAs) in higher education represents a transformative shift in pedagogical approaches, particularly within resource-constrained developing contexts. This study examines the effectiveness of AI-powered learning assistants in enhancing learning outcomes and student engagement in computer science education across six universities in Northwestern Nigeria. Employing a cross-sectional survey design, data were collected from 387 participants (86% response rate) comprising students, academic staff, and administrative personnel. The research addressed two primary objectives: assessing the effectiveness of AIPLAs in enhancing student learning outcomes and examining student engagement and satisfaction levels when using these tools. Findings reveal that AI learning assistants significantly enhance programming skills development (M=4.31, SD=0.76), with programming tutorials and guidance (M=4.42, SD=0.71) emerging as the most effective application. Substantial improvements were also observed in theoretical understanding (M=4.18), critical thinking abilities (M=4.09), and independent learning skills (M=4.06). However, social competencies including team collaboration (M=3.68) and communication skills (M=3.76) showed modest improvement. Regarding engagement, 63.3% of respondents demonstrated familiarity with AIPLAs, with 69.3% having used them occasionally, though only 26.1% reported frequent usage. ChatGPT dominated adoption (71.8%), followed by Google Bard/Gemini (40.3%). The highest-rated benefits were 24/7 availability (M=4.23) and instant feedback (M=4.18). A strong positive correlation emerged between perceived benefits and learning outcomes (r = .723, p < .01). Gender differences in usage patterns were identified (χ²=12.847, p=.025), highlighting equity considerations. The study demonstrates that accessible AI tools can partially compensate for instructional resource limitations while requiring thoughtful integration preserving essential human elements of education. Recommendations include developing clear institutional policies, investing in infrastructure, implementing gender-responsive interventions, and redesigning assessments to leverage AI appropriately while preserving collaborative learning opportunities. 


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