The Architecture of Roles in AI-Designed Group Activities: A comparative inductive analysis of novice and experienced teachers' differentiated instruction within the IAT framework

The architecture of roles in AI-designed group activities. A comparative qualitative study by Hossein Talebzadeh (2026) analyzing how 89 Iranian teachers (38 novice, 51 experienced) assigned roles in 178 AI-designed group activities within the Integrated AI Triad (IAT) framework. Using reflexive thematic analysis, the study introduces three constructs — Role Richness, Role Synergy, and Level-Role Alignment — and finds experienced teachers produced significantly richer, more interdependent, and more precisely ZPD-aligned role architectures, while novices often collapsed integrated projects into conventional assessments lacking product integration. The paper introduces "pedagogical prompt literacy" as a portable construct: AI output quality is fundamentally contingent on teachers' capacity to encode pedagogical intentions through Pedagogical Content Knowledge.

Overview

Designing effective cooperative group activities that differentiate by student readiness is cognitively demanding. This study asks whether teachers' career stage shapes how they use generative AI to design differentiated group activities — and why. Within the IAT framework, teachers are positioned as "Bilingual Learning Designers" who must encode pedagogical intentions into AI prompts.

Method

• Sample: 89 Iranian teachers (38 novice, 51 experienced) across four cohorts.
• Corpus: 178 AI-designed group activities within the IAT framework (two activity models: Integrated Multi-Level and Mixed-Ability).
• Analysis: Reflexive thematic analysis with hybrid deductive-inductive coding, double-coded with intercoder reliability; trustworthiness and positionality addressed.
• Novel constructs: Role Richness (number/variety of roles), Role Synergy (interdependence among roles), Level-Role Alignment (match between task demands and student readiness/ZPD).

Key findings

• Experienced teachers design richer role architectures. 85% of experienced designs had detailed role descriptions (43/51) versus 30% of novice designs (11/38); 78% vs. 30% had 3+ distinct role types. (Percentages reflect relative thematic prevalence, not inferential claims.)
• The largest gap is Role Synergy. 90% of experienced designs showed role interdependence (46/51) versus 35% of novice designs; novices frequently produced parallel tasks completable in isolation.
• Precise Level-Role Alignment. Experienced teachers calibrated tasks to readiness — 88% had appropriately challenging advanced tasks and 90% appropriate basic tasks, with 65% explicit ZPD-calibration versus 15% for novices. Novices often used quantitative differentiation (more questions) rather than qualitative variation in cognitive demand.
• The novice–expert paradox. Experienced teachers, despite less AI familiarity, produced superior designs — evidence that pedagogical expertise, not technical proficiency, drives effective AI integration. "Smart drunk intern" framing: AI output needs constant expert evaluation.
• Pedagogical prompt literacy. Experienced teachers encoded PCK into prompts (e.g., the R.A.F. — Role + Audience + Format — framework) with greater precision. AI amplifies teacher judgment rather than replacing it.
• Experience alone is insufficient. A negative-case analysis showed one experienced teacher (19 years) produced low-synergy, non-integrated roles — experience must be paired with professional learning on integrating differentiated contributions.

Practical implications

• Professional development should pair technical AI training with pedagogical reasoning. The findings suggest PD should build teachers' capacity to articulate pedagogical intentions, anticipate AI responses, and evaluate outputs against pedagogical criteria.
• Use the three constructs as a design heuristic. Teachers and researchers can evaluate differentiated group activities on Role Richness, Role Synergy, and Level-Role Alignment.
• Position teachers as co-designers, not tool users. Agency in AI-assisted design is conditioned by pedagogical expertise; building that expertise is the lever for equitable AI integration.

Connected Concepts

• Teacher AI Competency — the knowledge teachers need; pedagogical prompt literacy as a core component
• Learning Design — differentiated group activity design as the collaborative object
• Teacher Role — the teacher as "Bilingual Learning Designer"
• Educational Development — PD implications for teaching teachers to design with AI

Connected Articles

• Talebzadeh AI Green Education 2026 — same author's IAT-framework study of AI-assisted instructional design quality
• Choi Teacher AI Interaction Lesson Design 2026 — teacher experience × AI proficiency in lesson design interaction patterns
• Teacher AI Teaming Five Levels — levels of teacher–AI teaming
• Connected AI Lesson Planning Vietnam — AI-assisted lesson planning in K-12

Citation

Talebzadeh, H. (2026). The Architecture of Roles in AI-Designed Group Activities: A comparative inductive analysis of novice and experienced teachers' differentiated instruction within the IAT framework. Farhangian University, Tehran, Iran.