AI Interaction Patterns Review

• AI Interaction Patterns are systematic approaches to structuring human-machine exchanges using defined interaction primitives, modalities, and timing.
• Empirical studies employing log analyses, user research, and cognitive metrics validate these patterns to enhance efficiency and safety across diverse applications.
• Design implications call for balancing user expertise, feedback, explanation, and agency to optimize collaborative and adaptive human-AI systems.

AI Interaction Patterns encompass the systematic approaches, configurations, and behaviors through which humans and machines exchange information, make decisions, and collaboratively construct meaning or artifacts. These patterns operationalize the dynamics of agency, control, communication modality, temporal sequencing, feedback, and responsibility within human–AI systems. Current research delineates these patterns across a wide spectrum of domains, from conversational agents to multi-agent learning environments, creative LLM collaborations, code generation, and safety‐critical systems. The field is characterized both by mature taxonomies and emerging frameworks that account for the complexity and diversity of AI-augmented human activities.

1. Taxonomies and Formal Models of AI Interaction Patterns

Multiple recent works provide explicit, structured taxonomies and formal models to capture the gamut of AI interaction patterns.

• Interaction Primitives and Mid-Level Patterns: The message-passing model in (Tsiakas et al., 2024) abstracts human-AI exchanges into two primitive intents—provide and request—with three core types (input, output, feedback). These primitives compose into mid-level patterns such as class-selection, prediction-based-XAI, and informative-advice. Patterns are projected onto design axes: initiation (human/AI), modality (data, control, explanation, feedback), temporal structure, and agency, supporting systematic composition and comparison.
• Entity–Relation Graphs for GenAI: The Interaction-Augmented Instruction (IAI) model (Shen et al., 30 Oct 2025) expresses all human–GenAI collaborations as graphs over entities—Human, Interaction, Text Prompt, Augmented Instruction, Model, Artifact—with directed edges encoding the causal dataflow (e.g., H→T, T→G, G→A, H→I, I→Aug). It further distinguishes twelve atomic paradigms (e.g., Interactive Prompt Enhancement, AI-driven Prompt Suggestion, Artifact→Multimodal Instruction), supporting pre/post-invocation interaction, prompt–artifact synergy, and nested paradigm composition.
• User-Guided Interaction Patterns: Comprehensive domain taxonomies (Luera et al., 2024, Siu et al., 2023) catalog user-initiated interaction styles in generative systems, including multimodal prompting, selection techniques (single/multi/lasso), parameter manipulation (menus, sliders, explicit user feedback), and object transformation (drag & drop, block connections, resizing). Clarity around when and how to deploy each pattern is provided, emphasizing trade-offs between expressivity, control, and cognitive load.
• Systematic LLM Collaboration Patterns: Mapping frameworks (Li et al., 2024) score human-LLM interaction studies along axes of “Collaboration” (initiative distribution: tool→companion→agent) and “Creativity” (processing→creation), using k-means clustering to reveal four major modes: Processing Tool, Analysis Assistant, Creative Companion, and Processing Agent.

2. Empirical Insights: Workflow Analysis and Interaction Efficacy

Recent empirical work leverages large-scale log analysis, qualitative coding, user studies, and cluster analyses to characterize prevalence, effectiveness, and tradeoffs across interaction patterns.

• Developer-AI Prompt Patterns: “Recipe” and “Context & Instruction” patterns in code generation yield the highest output quality per iteration, whereas “Template” and free-form “Question” patterns trade off iteration efficiency and adaptability (DiCuffa et al., 2 Jun 2025). Quantitative metrics (Q: response quality, I: iteration count, E: efficiency) enable cross-pattern benchmarking.
• IDE Proactive Intervention Patterns: Developer studies (Kuo et al., 15 Jan 2026) confirm that timing (e.g., post-commit boundaries vs. mid-task) and context alignment are critical—boundary-triggered proactive interventions achieve 52% engagement and halve suggestion interpretation times compared to reactive chat. Dismissal rates signal the need for unobtrusive, boundary-synchronous pattern adoption.
• Debugging Conversation Patterns: Insert expansion (context-eliciting micro-dialogues) and facilitated turn-taking (follow-up suggestions) in AI debugging assistants dramatically improve bug resolution rates (5× over baseline), fault localization, and user confidence (Chopra et al., 2024). Integration of workflow-awareness (difficulty classification, phase switching) further optimizes dialogue flow.
• Feedback and Personalization in Mobile AI Apps: Empirical breakdown of 759 UI instances (Siu et al., 2023) reveals a predominance of “General Feedback” (58.5%), with “Minimal” (13.3%) and “Fine-Grained” (28.2%) deployed per task criticality. Dynamic, actionable guidance is essential for input-sensitive, uncertain AI features.

3. Cognitive and Social Dynamics Across Modalities

Effective interaction patterns must account for underlying cognitive costs, social perception, and mutual understanding.

• Communicative Adaptation and Naturalness Divergence: Users systematically modulate their language and behavior to increase AI comprehension (1.02 mean agreement), but do not achieve conversational “naturalness” (–0.57 mean), highlighting persistent limitations in AI-hosted Theory of Mind (Adkins, 2024).
• Engagement Modes in Open-Ended Human–LLM Collaboration: The exploration–exploitation (Holstein et al., 3 Apr 2025) and constructive–detrimental engagement axes structure four cognitive patterns. Highest-quality outcomes are observed for balanced exploration (E ≈ 0.4–0.6) and high constructive engagement (C > 0.8). Interventions that induce reflective “Why?” questions, challenge modes, or externalized rationales raise constructive engagement and depth.
• Emotion, Novelty, and Coordination in Storytelling: Human–AI turn-taking studies (Fundal et al., 18 Dec 2025) reveal limited affective convergence (r ≈ 0.21 for User→AI, insignificant for AI→User), wider semantic and narrative exploration by humans (slower decay in embeddings), and higher innovation (mean novelty 5.9 vs. 5.3 bits). Implications include supporting emotional autonomy and amplifying human-generated surprise, rather than enforcing AI-centric convergence.
• Social Dynamics, Curiosity, and Agency in Learning: Discourse analyses in educational contexts (Morris et al., 16 Jan 2026, Muzumdar et al., 29 Nov 2025, Hao et al., 3 Jun 2025) confirm transactional, information-focused AI patterns (low curiosity expression, offloaded critical engagement) unless systems explicitly scaffold high-agency, dialogic, or co-constructive behaviors. Peer interaction outperforms AI for curiosity and deep engagement, but carefully engineered co-construction and co-regulation patterns can close performance gaps for lower-knowledge students in multi-agent environments.

4. Interaction Patterns in Specialized and Multimodal Contexts

• Multi-Agent Learning Environments: Engagement patterns in systems with teacher, peer, and back-end AI roles stratify into co-construction (joint knowledge building), preferred by novices, and co-regulation (meta-cognitive orchestration), preferred by experts (Hao et al., 3 Jun 2025). Lag Sequential Analysis quantifies significant behavioral transitions, supporting real-time adaptation and personalized intervention orchestration.
• GenAI and Complex Artifact Manipulation: The IAI model and survey taxonomies (Shen et al., 30 Oct 2025, Luera et al., 2024) identify pre- and post-invocation interaction points, prompt–artifact synergy, and rich compositional interactions (block-based flow, multi-modal selection, variable prompt control) as central to creative and analytic workflows. Guidelines dictate matching interaction timing and form to user intent complexity and precision requirements.
• Game-Based Interaction: “Player–NN” frameworks (Zhu et al., 2021) recognize patterns such as AI as Apprentice, Competitor, Teammate, and Designer, each with distinct roles and transparency levels in-game. Empirical compliance with human–AI interaction guidelines (e.g., flow, discovery learning, invitation to play) helps transfer lessons from playful to goal-oriented systems.

5. Harmful Patterns and Control-Theoretic Safety Models

• Deceptive, Anthropomorphic, Opaque, and Seamless Patterns: Interface features that demand or obscure user control, anthropomorphize, suppress explainability, or optimize for frictionless consumption can distort autonomy and reinforce biases (Ibrahim et al., 2024). Examples include ChatGPT’s hidden data-sharing settings and TikTok’s discouragement of negative feedback.
• DECAI Framework: DECAI (Ibrahim et al., 2024) formalizes the human–AI–interface loop as a control system, where interface affordances A(d, t) transform both sensed user input and actuated AI output. Temporal feedback and adaptation gain (α) quantify the reinforcement of user behaviors, driving long-term impacts and harms. Model evaluation proceeds through heuristic audits, controlled experiments, and field deployments to measure H (harm cost) and recommend regulatory interventions.

6. Guidelines and Design Implications

• Pattern Selection and Composition: Designers should match interaction patterns to agency requirements, user expertise, and context-criticality, mixing data, feedback, and explanation primitives to enable contestability, transparency, and control (Tsiakas et al., 2024, Shen et al., 30 Oct 2025, Siu et al., 2023).
• Workflow-Aware Intervention: Proactive suggestions should anchor to workflow boundaries, support user autonomy, and surface concise rationales and confidence signals (Kuo et al., 15 Jan 2026, Chopra et al., 2024).
• Alignment with Learning Theories: Educational applications benefit from deliberate mapping of interaction patterns (directive, assistive, dialogic, empathetic) to behaviorist, cognitivist, constructivist, and humanist learning models, with attention to agency and scaffolding metrics (Muzumdar et al., 29 Nov 2025, Hao et al., 3 Jun 2025).
• Personalization and Peer-Mode Hybridization: Multi-agent and co-regulative patterns adaptively scaffold novices and challenge experts, integrating real-time analytics to orchestrate agent interventions and reduce performance disparities (Hao et al., 3 Jun 2025, Muzumdar et al., 29 Nov 2025, Morris et al., 16 Jan 2026).
• Transparency and Error Correction: Exposing model reasoning, supporting fine-grained edits, and foregrounding system limits counteract both cognitive and ethical pitfalls seen in current black-box and anthropomorphic patterns (Ye et al., 21 Jul 2025, Ibrahim et al., 2024).

By articulating and empirically validating a wide palette of AI interaction patterns—spanning formal design spaces, cognitive and affective dynamics, workflow efficacy, and safety risks—the field advances toward methodical, context-sensitive, and user-aligned AI system design. Patterns are not merely decorative: they encode the operational logic by which human and artificial agents can productively, safely, and creatively share cognitive labor.