Balalojanan, VVigneswaran, VSukirthan, TThayalan, NThiruthanigesan, KSamarakoon, URagel, R. G2026-11-042025V. Balalojanan et al., "AI Dependency in Sri Lankan Higher Education: Effects of Chatbot Overuse on Student Learning and Problem-Solving," 2025 IEEE 19th International Conference on Industrial and Information Systems (ICIIS), Peradeniya, Sri Lanka, 2026, pp. 249-254, doi: 10.1109/ICIIS69028.2026.11450932.979-833157036-1https://rda.sliit.lk/handle/123456789/5420Artificial Intelligence (AI) chatbots have rapidly transformed computer science education in Sri Lanka by offering instant coding assistance and conceptual explanations. While these tools enhance learning efficiency, their widespread adoption raises concerns about over-reliance and its impact on students' critical thinking and independent problem-solving abilities. This study investigates AI dependency among 80 computer science students from three Sri Lankan institutions during an open-book programming assignment conducted in May 2025. Students were allowed unrestricted access to AI chatbots and online resources, and their behavior was analyzed using screen monitoring, post-task interviews, and performance assessments. Results revealed that 88% of participants used AI chatbots even for basic tasks, indicating a significant trend of diminished self-reliance. Chatbot-reliant students demonstrated higher accuracy in syntax-based tasks but performed worse in algorithmic design and conceptual articulation. This study introduces the AI Dependency Index (AIDx), a novel metric to quantify the level of reliance across different task types. Findings show that 62% of students fall into the high dependency category, particularly among first- and second-year undergraduates. These insights highlight the need for pedagogical strategies that promote responsible AI usage rather than replace critical thinking and problem-solving skills in higher education.enAI in educationchatbotsritical thinkingeducational technologyproblem-solving skillsSri Lankan higher educationstudent learningConference Paperdoi: 10.1109/ICIIS69028.2026.11450932