## 4 Results  
### Results of the Qualitative Analysis  
Qualitative analysis proceeded in four sequential stages. First, transcripts of twenty GPT tutor sessions, provided by student participants in the research project, were converted to searchable text via OCR, and Croatian text was translated into English to ensure consistency in coding. Second, open coding was conducted by reading each of the 20 student-GPT tutor sessions line by line. Text segments reflecting either student behaviors or tutor actions were assigned to one or more of five a priori constructs: Engagement, Learning Environment, Teaching Presence, Social Presence, and Cognitive Presence. Third, axial coding clustered similar open codes into sub-themes under each construct; only sub-themes appearing in at least eight transcripts (\(\geq\)40%) were retained. Finally, for each sub-theme, we selected illustrative excerpts that preserved the original wording and context. The following themes were identified in the student transcripts.  
Student engagement: Two sub-themes characterized students' persistence and motivation.
* Persisting through challenge appeared in 90% of transcripts. Students who scored poorly on initial quizzes immediately opted to retry, demonstrating notable behavioral persistence. One student noted, \"Quiz 1 (result: 2/5)...agreed to retake the quiz a second time.\"
* Mastery and completion were present in 85% of sessions. In approximately 85 percent of the transcripts, students kept working until they had the perfect score on the final quiz, as shown by the frequent evaluations in the transcripts: \"FINAL QUIZ:... Result 7/7\".  
Learning-environment quality: Two sub-themes captured the tutor's responsiveness and contextualization of AI-led teaching.  
* Immediate, just-in-time feedback was present in 95% of transcripts. The GPT tutor returned instant confirmation or a gentle correction (\"Correct! Time for the quiz\") within seconds, so students were sure whether they were on the right learning track.
* Contextual examples appeared in 80% of sessions. The GPT tutor nudged students to apply new ideas to fresh situations: \"Can you imagine another real-world example where such record-keeping would be useful?\"
* Teaching presence: Two sub-themes illustrated the tutor's instructional structure.
* Guided questioning was identified in all transcripts. Every session unfolded through guided questioning, starting with everyday scenarios (\"Imagine you have bought a product online...\") and then walking the student step-by-step toward the underlying concept.
* Clarifying misconceptions was found in 60% of all analyzed sessions. In these cases, the tutor paused, clarified, and praised the corrected answer (\"Bravo!\"), giving students the confidence to move forward.
* Social presence: Two sub-themes reflected the tutor's interpersonal tone.
* Encouraging tone was evident in around 75% of transcripts. Frequent positive reinforcement--\"Tutor: Bravo!\"--fostered a warm, conversational atmosphere.  
* Personalized interaction was present in approximately 40% of sessions. While AI did not use students' names, it did remember their previous mistakes and accordingly adapted its approach. Cognitive presence is shaped by two sub-themes, which can be formulated in terms of 'conceptual application' and'reflective problem-solving':
* Conceptual application was observed in 85% of transcripts. Students synthesized technical concepts in their own words, for example: \"Student: Transparency, authenticity, fraud reduction.\"
* Reflective problem-solving emerged in 70% of sessions when the tutor's prompts elicited deeper reasoning: \"Tutor: Why is it useful that records cannot be changed?\" \"Student: Because the system stays secure.\" Across the 20 transcripts, each construct and its sub-themes were well covered, with Teaching Presence and Learning-Environment Quality identified in almost all the chats, followed by Engagement (90%) and Cognitive Presence (85%), with Social Presence present in two-thirds of the analyzed chats.