Collaborative Teaching in AI-Driven Software Engineering

• Collaborative Teaching Approach is a method that uses adaptive, role-aware AI interfaces to support dynamic interactions between software developers and intelligent systems.
• It leverages developers' mental models and role attribution to enable real-time switching between support functionalities and expert feedback for enhanced performance.
• Empirical studies show that explicit role framing boosts perceived usefulness and ease of use, resulting in greater AI adoption in software engineering.

Collaborative Teaching Approach in Artificial Intelligence–Powered Software Engineering

The collaborative teaching approach in the context of AI-powered software engineering refers to adaptive, role-aware interfaces and workflows that allow developers and AI systems to interact as co-participants—sometimes as inanimate tools and sometimes as human-like teammates. The approach leverages developers' mental models, fosters mutual alignment through role flexibility, and supports technology acceptance and adoption by explicitly mapping system behavior to user expectations. Recent research integrates psychological, empirical, and design principles to dissect and optimize these collaborations, with an emphasis on both cognitive and social dimensions (Zakharov et al., 29 Apr 2025).

1. Conceptual Foundations: Mental Models and Role Attribution

Developer–AI collaboration is fundamentally shaped by the mental models that engineers hold regarding AI systems. These models determine expectations, trust, and usage patterns. Two dominant conceptions have been identified:

• AI as Inanimate Tool: Developers view AI systems as deterministic, pattern-matching engines or enhanced search utilities. This group demands rigorous technical guarantees, minimal hallucination, and code/style consistency.
• AI as Human-Like Teammate: AI is anthropomorphized as an assistant, peer, or expert advisor. Here, occasional errors are tolerated as with human colleagues, and the system’s rapid iteration and offloading capacities are valued.

Factor analysis of empirical interview and survey data yields a taxonomy of AI roles in SE:

Role Group	Example Roles	Loading (l) on Factor
Support	Assistant, Reference Guide, Tool	0.657, 0.506, 0.495
Expert	Problem Solver, Advisor, Reviewer	0.599, 0.662, 0.691

Correlation analyses demonstrate that developers who attribute more, and more diverse, roles to AI perceive higher usefulness ($r = 0.59$) and ease of use ($r = 0.56$). Both support and expert roles independently boost these adoption metrics (Zakharov et al., 29 Apr 2025).

2. Interaction Modalities and Adaptive Collaboration

Recent taxonomies characterize how these role attributions manifest in concrete workflows:

• Support Role Interactions: E.g., auto-complete suggestions, reference lookups, documentation augmentations, triggered by user input and responding with brief, contextually relevant outputs.
• Expert Role Interactions: E.g., evaluative feedback, design critique, architectural guidance, triggered by natural-language dialogue or explicit requests, delivering high-level insights or peer-review assessments.

Collaborative teaching interfaces adaptively switch between these interaction types. Mode selection, customization of AI formality, verbosity, and autonomy, as well as mechanisms for real-time role-switching (e.g., toggling between lookup and advice modes) are central design strategies. Onboarding flows selectively frame the AI as junior colleague or senior advisor depending on workflow phase and user preference (Zakharov et al., 29 Apr 2025).

3. Empirical Assessment: Quantitative and Qualitative Insights

Empirical studies employ mixed-method designs—interviews, surveys, and log analyses—to quantitatively link collaborative teaching features with adoption outcomes. Pearson correlation coefficients between AI role diversity and classic Technology Acceptance Model (TAM) metrics such as Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) are robust, with expert-role attribution correlating especially strongly ($r = 0.41$–$0.42$) (Zakharov et al., 29 Apr 2025).

Qualitatively, collaborative sessions illuminate the importance of explicit role definition, iterative communication, and documentation. The transition from AI as mere code-completion tool to AI as knowledge-sharing partner underlines the teaching dimension, integrating interactive learning cycles and prompting knowledge transfer. Human oversight in security, debugging, and complex logic remains indispensable; collaborative teaching does not imply unconditional delegation (Hamza et al., 2023).

4. Design Strategies for Teaching-Oriented AI Systems

Effective collaborative teaching approaches require adaptive, personalized interfaces and workflows:

• Mode Customization: Allow users to select technical assistant (assertive/infallible) versus collaborative partner (conversational/tolerant) modes.
• Personalization and Memory: Track user preferences for role, interaction style, and detail level; adapt suggestions accordingly over time.
• Role-Switching: Enable dynamic task-dependent transitions between support and expert interaction modes.
• Framing and Onboarding: Initial presentation of AI as a “junior colleague” mitigates over-reliance, while “expert advisor” framing boosts trust in high-stakes tasks.
• Feedback Integration: Continuous collection of user ratings and comments, linked to prompt/version histories, enforces an iterative model-place—and supports ongoing learning (Zakharov et al., 29 Apr 2025).

5. Impact on Adoption, Productivity, and Team Dynamics

Assigning collaborative teaching roles to AI demonstrably increases both perceived utility and ease of use. Adaptive, role-sensitive AI interfaces foster greater adoption, especially when users can calibrate both the autonomy and the personality of the AI system. Multimodal workflows (support and expert) separately strengthen acceptance, and onboarding strategies that match user experience levels further shape trust trajectories.

At the team level, the promise of collaborative teaching extends to distributed software engineering, where shared mental models and transparent role allocations may reduce coordination friction, enhance knowledge sharing, and boost overall productivity. Longitudinal studies are required to track the evolution of mental models, trust dynamics, and objective performance over extended periods in real-world projects (Zakharov et al., 29 Apr 2025).

6. Future Directions and Open Research Challenges

Long-term efficacy and scalability of collaborative teaching approaches remain open questions. Key research priorities include:

• Longitudinal Model Evolution: Tracking how developer mental models and role attributions change with sustained AI use.
• Controlled Onboarding Experiments: Isolating the impact of initial framing and role allocation on trust, reliance, and productivity.
• Team-Level Analysis: Investigating how collaboratively constructed AI mental models shape distributed collaboration, conflict resolution, and group decision quality.
• Objective Productivity Metrics: Linking collaborative teaching design features to measurable delivery speed, code quality, and error rates.

A plausible implication is that collaborative teaching approaches will become foundational to future AI4SE tool development—shifting focus from narrow task completion to holistic, role-aware partnership and explicit pedagogical interaction (Zakharov et al., 29 Apr 2025).