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@article{210489,
        author = {Paul Joseph Lawrance},
        title = {Learning Python through AI-Assisted Code Generation and Classroom Discussion: Developing Computational Thinking and Problem-Solving Skills among BSc Data Science Students},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {6},
        pages = {1174-1185},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=210489},
        abstract = {The increasing availability of generative artificial intelligence (AI) tools has created new opportunities for teaching and learning programming. However, using AI-generated code effectively requires learners to develop a strong understanding of programming concepts, logical reasoning, and problem-solving skills. This study examines an AI-assisted code-generation and classroom-discussion-based instructional approach to teaching Python programming among 11 second-year Bachelor of Science (BSc) Data Science and Analytics students at Mary Matha Arts and Science College, Periyakulam, Tamil Nadu, India. Adopting a mixed-method research design, the study investigated students’ learning outcomes, particularly in Python programming knowledge, computational thinking, problem-solving skills, and practical programming competence, while also exploring their perceptions of the institutional teaching-learning facilities. Data were collected through classroom observations, student interactions, six practical assessments, class tests, internal examinations, model examinations, practical examinations, and an end-of-semester feedback questionnaire. The instructional approach integrated AI-powered tools, including ChatGPT and Google Gemini, with teacher-guided classroom discussions, code interpretation, program execution, and debugging activities. The findings revealed an overall mean score of 4.48 out of 5 across 66 observations from six AI-assisted practical assessments, indicating a high level of correctness in program outputs. Furthermore, all students scored above 80% in both the model and main practical examinations, demonstrating their ability to apply Python programming concepts to practical tasks. However, theoretical examination performance varied, with only five students achieving above 80%, highlighting the need for continued support in conceptual understanding, independent reasoning, and theoretical knowledge. Student feedback was unanimously positive, with 100% of respondents strongly agreeing that AI-assisted code generation, classroom discussions, individualized attention, and the available teaching-learning facilities supported their learning. The study highlights the educational potential of integrating generative AI with interactive teaching strategies and hands-on programming practice. It emphasizes that AI-generated code should serve as a learning resource rather than a substitute for programming knowledge, independent reasoning, and critical evaluation.},
        keywords = {Artificial Intelligence in Education; AI-Assisted Programming; Python Programming; Computational Thinking; Problem-Solving Skills; Generative AI; Classroom Discussion; Practical Programming Competence; Higher Education.},
        month = {November},
        }