2026 ASEE Annual Conference & Exposition

The incorporation of Artificial Intelligence (AI) into Computer Science (CS) and Information Systems (IS) curricula significantly augments student engagement, enhances cognitive proficiency, and fosters superior learning outcomes. Moreover, it equips learners with the competencies required to employ AI technologies ethically and effectively within the globalized digital workforce.
This study addresses two principal dimensions. The first pertains to the accelerated evolution of generative AI, which has introduced unprecedented opportunities and complex challenges across educational environment predominantly in the domain of tailored and adaptive learning. The advent of Large Language Models (LLMs) has redefined instructional methodologies by offering sophisticated framework capabilities, wherein these models emulate human tutoring dynamics. Accordingly, this paper conducts a comparative analysis between AI-driven tutoring systems and traditional human tutoring for learners in computer science discipline.
The second dimension concerns the educational integration of Generative Artificial Intelligence (GenAI) technologies, which presents transformative potential for enriching academic engagement and learning experiences across multiple educational tiers. The present research investigates this potential by analyzing the pedagogical implications of embedding AI tools within computer science programs. The empirical dataset employed in this study comprises authentic student AI interactions derived from engagements with a Large Language Model (LLM)-based virtual assistant across various course assignments. These data chronicle the communicative and problem-solving exchanges between students and ChatGPT-like systems. The analysis aims to elucidate distinctions in interactional behavior and learning efficacy, culminating in evidence-based recommendations for optimizing the incorporation of LLM technologies within contemporary educational frameworks.