# Artificial Intelligence (ChatGPT) and Bloom's Taxonomy in Theoretical Computer Science Education
## 6 Conclusions  
In our research, we investigated the strengths and weaknesses of artificial intelligence (AI) in assessing learning outcomes, with a specific focus on theoretical computer science. Through experiments involving the ChatGPT language model, we analyzed AI's ability to address various types of educational tasks, particularly those from the domain of formal languages and automata. Our findings revealed distinct performance patterns linked to [PERSON]'s taxonomy of learning tasks. While AI excels in tasks requiring memorization and straightforward application, it encounters challenges when addressing tasks that demand deeper understanding, critical evaluation, and creative problem-solving. Notably, human participants retain a competitive edge in these more cognitively demanding tasks, albeit with variations depending on their specific skills and prior knowledge. Despite these promising insights, our study has inherent limitations, including a relatively small sample size and a focus on students from a single university. In phase one, we worked with multiple informatics specializations, while in phase two the sample was adjusted to focus solely on the HU group (students without prior grades) to achieve more meaningful statistical outcomes. These constraints suggest that our results should be regarded as an initial exploration of AI's role in performance comparisons based on [PERSON]'s taxonomy. Future studies with larger, more diverse sample groups could validate and extend these findings, offering a more comprehensive understanding of AI's potential in education. Our research highlights actionable recommendations for educators and evaluators, emphasizing the importance of designing learning tasks that are well-aligned with their intended cognitive objectives. A deliberate approach to balancing task types within assessments could significantly improve their efficacy in measuring student learning outcomes. Given the rapid advancements in AI and its growing integration into education, our conclusions serve as a call to reimagine educational strategies. Teachers must not view AI as a competitor or a tool to be restricted, but as a partner in enhancing pedagogy. A nuanced approach to teaching and evaluation--grounded in thorough analysis and careful task design--can ensure that AI complements, rather than undermines, human learning. Looking ahead, we believe that AI holds an irreplaceable role in modern education. By fostering collaboration between human and machine intelligence, educators can craft more effective, equitable, and innovative learning environments. Such an approach can transform not only how knowledge is imparted but also how the next generation of learners engages with and contributes to their fields of study. Our research systematically examined the interplay between artificial intelligence (AI) and human performance in educational tasks, with a focus on theoretical computer science. Through a two-phase experimental study utilizing the ChatGPT models (GPT-3.5 and GPT-4.0), we analyzed AI's capabilities across [PERSON]'s taxonomy of learning tasks, comparing them with student performance in tasks involving formal languages and automata. The second stage of our research allowed for more refined analyses due to an increased sample size and adjustments to the experimental design. These refinements enabled clearer comparisons between human students (HU) and AI models (GPT-3.5 and GPT-4.0). The findings revealed nuanced differences in performance based on the learning task type (LTT):  
Remembering (Type 1): GPT-4.0 consistently outperformed human participants, demonstrating its strength in tasks requiring rote memorization and retrieval of information. Understanding (Type 2): Human participants excelled over GPT-3.5, with students showcasing better contextual grasp and conceptual clarity.