Student-GenAI Dialogue Transcripts

• Student-GenAI Dialogue Transcripts are systematically captured records of annotated interactions between students and AI agents in educational settings.
• They employ structured data formats and detailed annotation schemas to enable both quantitative metrics and qualitative coding of dialogue patterns.
• The analysis of these transcripts informs adaptive scaffolding, self-regulated learning enhancements, and the design of context-aware GenAI tutors.

Student-GenAI Dialogue Transcripts refer to the systematically captured, annotated, and analyzed records of conversational exchanges between human students and generative artificial intelligence agents within educational contexts. These transcripts serve as empirical traces for learning analytics, supporting both methodological advancements and practical system design. Across multiple domains—programming, clinical practice, academic writing, analytics feedback—the corpus structure, coding schemas, transcript patterns, and research applications reflect the evolution of dialogic AI-mediated learning.

1. Data Structures and Annotation Schemas

Student-GenAI dialogue transcripts are typically captured in structured formats that facilitate both large-scale quantitative analysis and fine-grained qualitative coding. In recent datasets such as “GenAI-Assisted Information Problem Solving in Education” (Li et al., 19 Jan 2026), each exchange is stored in a CSV file, where a row encodes both the student prompt and GenAI response. Key fields include user_id (pseudonymised), user_ask_time and chatbot_utterance_time (ISO timestamps), user_utterance_text and chatbot_utterance_text (raw text or JSON array), and categorical annotation codes (e.g., user_utterance_code for student intent; chatbot_utterance_code for response typology). Ancillary logs may cover system events, essay drafts, or survey responses.

Annotation schemas are two-level: student utterances are coded for intent (e.g., Knowledge, Judgement, Revise, Inspire, Task Clarification, Writing Advice), while GenAI responses can carry multi-label tags (Fact, Suggestion, Critique, Clarify, Chitchat). Dual coding and adjudication yield high reliability (Cohen’s κ up to 0.92). Categorical frequencies and intent confusion matrices are reported, supporting methodological rigor.

In domains such as clinical pharmacy (Wei et al., 4 Dec 2025), utterance-level codes draw from models like Calgary–Cambridge, spanning Routine Question (RQ), Checking with Patient (CP), Specifying Symptoms (SS), Recognising & Responding to Relevant Information (RRRI), Professional Instructions (PI), Statement (ST), and Chitchat (CC). LLMs are fine-tuned for annotation (LLaMA-3 8B, F1 ≈ 0.805).

2. Methodologies for Transcript Collection and Analysis

Transcript collection leverages multimodal input modalities—typed text, voice (e.g., via OpenAI Realtime API (Jacobs et al., 12 Sep 2025)), or audio recordings (InterviewBot (Wang et al., 2023)). Preprocessing involves anonymization, denoising, and exclusion of invalid sessions. For audio, transcription systems (e.g., Whisper-large-v3-turbo, RevAI) are manually corrected for diarization accuracy (F1 up to 93.6%). Tokenization and filtering target spontaneous speech phenomena and background noise.

Analysis uses a blend of quantitative statistics (turn counts, session length, lexical diversity) and advanced learning analytics techniques: Epistemic Network Analysis (ENA) models co-occurrence patterns among dialogue codes in moving windows; Sequential Pattern Mining (SPM, e.g., TraMineR seqefsub) detects frequent temporal sequences, constrained by empirical stability and statistical significance.

Lag Sequential Analysis (LSA) computes transition probabilities P(i→j) among coded utterances, supporting identification of engagement and co-regulation behaviors in multi-agent environments (Hao et al., 3 Jun 2025). Statistical separation of behavioral networks is established via Mann–Whitney U, χ², Cramér's V, and effect sizes (e.g., rank-biserial correlation r).

3. Transcript Patterns: Engagement, Inquiry, and Error Resolution

Canonical transcript patterns recur across studies:

• Iterative Refinement Loop: Typical in information problem solving, where students pose a Knowledge query, receive a factual GenAI response, and then issue follow-up (Revise) or clarification prompts (Li et al., 19 Jan 2026).
• Co-Construction and Co-Regulation: In multi-agent setups, transcripts reveal sequences such as Student Ask Question → AI Peer Respond → Student Negotiate/Confirm; these promote knowledge construction and facilitate self-regulated learning (Hao et al., 3 Jun 2025). High prior-knowledge students exhibit increased co-regulation and meta-cognitive control (SB5, SB6 codes).
• Debugging and Error Resolution: In programming contexts, transcripts often consist of error-message copies followed by targeted GenAI diagnosis and correction (e.g., TypeError in Python, feedback on IndentationError in voice tutors (Jacobs et al., 12 Sep 2025, Amoozadeh et al., 2024)).
• Inquiry Patterns in Clinical Practice: High-performing students interleave recognition (RRRI), statements (ST), rapport-building (CC), and focused clinical questions, while low performers remain confined to routine procedural loops (RQ, CP, SS) (Wei et al., 4 Dec 2025).

A representative pattern matrix (from (Hao et al., 3 Jun 2025)) is shown below:

Student Code	% Overall	High Prior %	Low Prior %
SB1 (Ask)	48.19	45.2	55.3
SB3 (Initiate)	13.84	15.5	12.1
SB5 (Regulate)	14.09	12.9	11.2
SB6 (Manage Partners)	5.66	7.5	4.5

4. Domain-Specific Transcript Features and Limitations

Voice-based GenAI tutoring (Jacobs et al., 12 Sep 2025) yields novel transcript modalities, with observed issues in code speech generation (LAI: “language incorrect” or garbled verbalization of code primitives) and repetitive output (REP), resulting in overall correctness rates of 71.4%. Pair-programming prompts and requests for “natural language” code explanations are distinguished from direct debug queries.

MBA clinical dialogues (Wei et al., 4 Dec 2025) display inquiry-trajectory differences by demographic: First-language English speakers utilize rapport and structural statements more, while EAL learners favor verification and procedural triangles. Work-experience and institutional context further shape sequential pipelines, e.g., professional instructions following routine questions (SS–RQ–PI in Malaysian students).

Learning analytics dashboard dialogues (Uzun et al., 8 Jan 2026) are characterized by the SRL level of the student: Clarification and reassurance for low-SRL, technical and personalized planning for high-SRL. GenAI assistants perform best on clarity (mean rating = 4.63), less so on personalization and error handling.

Transcript length and verification behavior vary: CS1 students infrequently test edge cases, leading to help-seeking behaviors that bypass concept mastery (Amoozadeh et al., 2024).

5. Accessibility, Licensing, and Usage

Major datasets are open-source under BSD-2-Clause or similar licenses (Li et al., 19 Jan 2026), with anonymous user identifiers, documented biographic and prior-knowledge surveys, and full session artifact release (writing logs, proposals). Loading samples are provided in both Python and R, supporting reproducibility and cross-platform analytics.

Ethical oversight is documented, with informed consent, right to withdrawal, and explicit anonymization protocols. Usage patterns in voice-enabled settings are explicitly connected to accessibility for visually impaired students, with recommendations for customizable verbosity and multimodal feedback integration (Jacobs et al., 12 Sep 2025).

6. Research Applications and Implications

Transcript analytics address multiple research frontiers:

• Self-Regulated Learning (SRL): Dialogic trace features (reformulation rates, uptake of Inspire moves) are mapped to SRL model phases (planning, monitoring, evaluation).
• Adaptive Scaffolding and Engagement: Transcript-derived metrics (e.g., engagement score E = α * r_form + β * (#Inspire) – γ * (#Chitchat)) enable dynamic scaffolding and near real-time intervention (Li et al., 19 Jan 2026).
• Learning Gains and Quasi-Experimental Analysis: Linkage of transcript features to performance scores (ΔLG = Score_post – Score_pre) facilitates causal inference under self-selected GenAI usage, via propensity score matching and regression (Li et al., 19 Jan 2026).
• Personalisation and Diffentiation: Transcript patterns guide agent role adaptation; novices benefit from cognitive scaffolds, experts require reflective challenge. Co-construction sequences are positively correlated with learning gains (r ≈ 0.42 for low prior knowledge group) (Hao et al., 3 Jun 2025).
• Limitations and Design Recommendations: Early transcript termination (“early endings”) and shallow follow-up in admission interviews suggest needs for structured topic-flow curricula and long-context memory modules (Wang et al., 2023).

Transcript research continues to inform the design of adaptive, context-aware GenAI tutors, equity monitoring in diverse cohorts, and the operationalization of dialogic pedagogy at scale.

7. Representative Dialogue Excerpts

To illustrate typical transcript structure and interaction dynamics, below are canonical excerpts:

Programming, Debugging:

• Student: “Meine Schleife gibt immer noch ’IndentationError’. Was mache ich falsch?”
• GenAI (voice): “Der Fehler liegt an der Einrückung der if-Bedingung. Verschiebe sie eine Stufe tiefer in die Schleife, dann sollte es laufen.” [Correct mistake explanation + next-step.]

Information Problem Solving (FLoRA):

• Student: “What is a prediction model?” [Knowledge]
• GenAI: “A prediction model uses statistical methods to forecast…” [Fact]
• Student: “Can you explain how it works with time-series data?” [Revise]
• GenAI: “Sure—time-series models like ARIMA…” [Suggestion]

Clinical Inquiry:

• Student: “Are you still taking the Atorvastatin 20 mg at night for cholesterol?” [SS]
• VP: “That’s correct, 20 mg at night. How long have you been taking the Atorvastatin?” [RRRI]

Analytics Feedback (SRL-dependent):

• Low-SRL Student: “What are my overall strengths and weaknesses?”
• GenAI assistant: “Your strengths include regular engagement with course resources… your weaknesses seem to lie in debate and video interaction.”

Multi-Agent Co-Construction:

• Student: “How does the Notetaker agent integrate visual diagrams?”
• AI Peer–Notetaker: “I automatically convert slide images into bullet points by extracting captions and annotations.”
• Student: “Ah, so if the slide has embedded equations, those get text-parsed as LaTeX code?” [Negotiate/Confirm]

These excerpts demonstrate the diversity, structure, and intent-driven coding of authentic educational student-GenAI transcript records.