Artificial Intelligence (ChatGPT) and Bloom’s Taxonomy in Theoretical Computer Science Education - Hashim Habiballa; Martin Kotyrba; Eva Volna; Vladimir Bradac; Martin Dusek (Applied Sciences)
The study focuses on evaluating the performance of AI-based tools, specifically ChatGPT versions 3.5 and 4.0, in comparison to human students in the field of Theoretical Computer Science Education. The experiment aims to assess the capabilities of both AI and human subjects in solving learning tasks based on Bloom’s Taxonomy. The primary objectives of the study are to determine the educational performance of AI and students, identify areas where students may outperform AI in learning tasks, and evaluate the normalized overall results of educational performance with equal assignment weights. The assessment included testing on various types of Bloom’s Learning Task Taxonomy (LTT) based on specific Learning Objectives (LOs). Hypotheses were formulated for quantitative and qualitative analysis to compare the performance of AI and human subjects. Quantitative analysis revealed engaging results regarding the educational performance evaluation with AI-based tools. While some differences were observed in the performance of AI and students, the normalized overall results with equal assignment weights did not show statistically significant differences. The study highlighted the advantages and disadvantages of both humans and AI bots in solving learning tasks. The study concludes that there are areas where students may have an advantage over AI in tasks requiring understanding, evaluation, and creative thinking. Recommendations are provided for educators on utilizing AI-based tools in education, emphasizing the coexistence of AI possibilities with well-designed assignments. The findings suggest that AI has a valuable role in education, and a thorough analysis of teaching approaches and student evaluation is essential in leveraging artificial intelligence effectively.