# Artificial Intelligence (ChatGPT) and Bloom's Taxonomy in Theoretical Computer Science Education
## 5 Results and Didactic Recommendations  
Based on the analysis of experimental data, we can summarize the detailed results from previous chapter. **H1. ** The overall results of the educational performance of AI and students are equal. (_Quantitative_)  
Phase 1: Zero hypothesis H1 rejected (significant differences between groups--HUA, HUB, HUC, HUD, GPT35, GPT40). * statistically significant differences especially between group **HUA (students with grade A) and both GPT3 and GPT4 bots**. * additional significant difference observed between **HUB (students with grade B) and GPT3 bot**. * overall results are nominally better between humans of all categories and both GPT 3 and GPT4. Phase 2: Zero hypothesis H1 rejected (significant differences between groups--HU, GPT35, GPT40). * statistically significant difference between group **HU and GPT35**. * in contrast to phase 1, no statistically significant difference between HU and GPT40, but HU had nominally better results. * The results of the educational performance of AI and students in particular types of Learning Objectives are equal. (_Quantitative/Qualitative_) Phase 1: Zero hypothesis not rejected in all types (small sample and six factor values). * nominally some statistically insignificant results (described in Table 6). Phase 2: Zero hypothesis rejected in all types (larger sample and only three factor values HU, GPT35, GPT40--Table 13). * Type 1--Remembering: GPT40 significantly better than HU
* Type 2--Understanding: HU significantly better than GPT35
* Type 3--Applying: GPT40 significantly better than HU
* Type 4--Analyzing: GPT40 and HU significantly better than GPT35
* Type 5--Evaluating: HU and GPT40 significantly better than GPT35
* Type 6--Creating: HU significantly better than GPT35 and GPT40
* There is a difference between the results of AI and students in particular LTT types. (_Qualitative_) Phase 1: Only nominal differences
* GPT--excellent explanations, practical application results
* HUA, HUB--understanding of context, tricky assignments, creative task with complex and connected operations Phase 2: Both nominal and statistically significant differences--in types (best to worst):
* Type 1--Remembering: GPT40, GPT35, HU. * Type 2--Understanding: HU, GPT40, GPT35. * Type 3--Applying: GPT40, GPT35, HU. * Type 4--Analyzing: GPT40, HU, GPT35. * Type 5--Evaluating: HU, GPT40, GPT35. * Type 6--Creating: HU, GPT40, GPT35. **H4. ** The normalized overall (same weight of assignments) results of the educational performance of AI and students are equal. (_Quantitative_)  
* Zero hypothesis not rejected (only phase 1)--non-weighted tasks. We would like to provide some narrating instructions for how to combine a modern AI-based language model in education. We do not favor the opinion that AI should be banned or restricted in student's performance evaluation. AI-based tools could be very useful to students in the process of learning and even in performance evaluation. Although the presented research provides only a narrow survey into very specific and broad branchesof Computer Science Education, we would like to highlight some points in case a specific educator allows the use of these tools, even in the process of evaluation. We believe that the problem of using AI-based tools in performance evaluation should not be perceived by means of restriction, but rather with the coexistence of these possibilities through well-designed assignments. The assignment should emphasize tasks with typical human predominance, such as understanding, evaluation of the problem, and creative design. This might apply only to some areas of IT education, e.g., AI can be very successful in programming complex solutions directly implementable in real-world situations. Recommendations for educators:  
1. Analyze new or established assignments through learning task taxonomy, and emphasize tasks requiring type 2, 5 and 6 (Understanding, Evaluation and Creation). 2.