Collaborative Problem Solving (CPS)

Updated 23 November 2025

Collaborative Problem Solving (CPS) is the process where human, AI, or hybrid agents exchange information, coordinate perspectives, and iteratively construct solutions to complex issues.
It leverages cognitive, social, and affective dynamics through methodologies like group reasoning, multimodal analysis, and reinforcement learning to optimize collaboration.
Applications span education, organizational behavior, and human–AI interaction, employing metrics, behavioral annotation, and graph-based optimization for robust measurement.

Collaborative Problem Solving (CPS) is an umbrella term for processes in which multiple agents—human, artificial, or hybrid—exchange information, synchronize perspectives, and jointly construct solutions to complex problems. CPS is fundamentally cognitive, social, and sometimes affective, and is central to research domains such as education, organizational behavior, human–AI interaction, and computational social science. Current CPS inquiry spans controlled laboratory tasks, platform ecosystems (e.g., Kaggle), multimodal and automated behavioral analysis, and algorithmic mechanisms for group formation and agent coordination.

1. Foundational Theories and Models of CPS

The classical conception of CPS is rooted in the cognitive science of group reasoning and collective intelligence. Miyake’s “constructive interaction” posited that dyads (and by extension, groups) co-construct understanding neither reducible to the sum of their members nor replicable by a single agent. Central theoretical motifs include:

Constructive interaction: A process in which participants problem-solve by taking complementary perspectives, correcting or elaborating on one another's ideas, and generating solutions unreachable by an individual alone (Hayashi et al., 2021).
Collaborative cognitive processes: As elaborated by Miwa and Ishii, group cognition involves iterative refinement, knowledge pooling, and distributed sense-making.
Perspective-switching: Hayashi, Miwa & Morita highlight that cognitive synergy is often sparked by agents taking different stances or introducing heterogeneous data—a principle operationalized in contemporary platforms and AI-driven systems.

No universally accepted formal mathematical definition of CPS exists beyond qualitative, process-oriented models that emphasize data/knowledge exchange, viewpoint-switching, and remixing of siloed resources (Hayashi et al., 2021). This “three heads are better than two” ethos underpins both classical and modern instantiations of CPS.

2. Measurement and Quantification: Datasets, Metrics, and Algorithms

Rigorous analysis of CPS demands both engineered datasets and robust measurement protocols:

Platform-scale metrics: On Kaggle, CPS is quantified by population-level statistics such as follower-degree distributions (power-law, exponent α ≈ 2.12), dataset-reuse frequency, and tiered activity levels. Detailed CSV schemas include user performance tiers, kernel-dataset linkages, and explicit remix counts (Hayashi et al., 2021).
Behavioral annotation frameworks: Dialogue-based CPS datasets use multi-level coding schemes. For example, social-cognitive and affective subskills (SS1–SS10, SC1–SC2, AS1–AS3) are annotated in utterance-level corpora (Wong et al., 19 Jul 2025, Wong et al., 21 Apr 2025). Coders compute Cohen’s κ for reliability and use human–AI ensemble pipelines to boost coverage.
Interaction graphs and RL: CPS can be conceptualized as a graph optimization problem, where each participant is a node and edge weights reflect shared thematic codes or interaction strength. Reinforcement learning (e.g., MADDPG) is employed to optimize group connectivity, degree variance, and minimize dominant-subgroup effects (Fang et al., 15 Mar 2024).
Multimodal feature extraction: Automated CPS assessment leverages text and acoustic embeddings (BERT, Wav2Vec2.0, openSMILE), prosodic measures, and turn-taking indicators as features for classification (Wong et al., 19 Jul 2025, Wong et al., 21 Apr 2025, Venkatesha et al., 6 Jul 2025).

These metrics and algorithms support both macro-level analyses (who contributes, how data flows, which subgroups drive solutions) and micro-level CPS skill detection (which utterance expresses negotiation vs. planning, or which team member is driving progress).

3. System Architectures and Environment Design

Multiple architectures operationalize CPS, integrating computational and social scaffolds:

Data platforms (Kaggle): CPS is fostered by visible, remixable artifacts (kernels), tiered user networks, reputation-driven leaderboards, and open data onboarding. Forums and notebook versioning ensure transparency and traceable collaborative lineage (Hayashi et al., 2021).
Collaborative learning tools (SciNote): CPS environments implement phases such as hypothesis formulation, evidence gathering, and argument construction. These stages are scaffolded by mini-paper creation, citation/mapping, and rubric-based evaluation; cross-platform data integration is enabled via backend plugins (Rafner et al., 2021).
Hybrid human–machine frameworks: CPS with AI agents employs natural language parsing engines (Spatial-AMR), concept learners (GOCI), and hierarchical task network (HTN) planners (e.g., JSHOP2), with bidirectional dialogue and clarification rounds to resolve under-specification (Kokel et al., 2022).
Task generators and games (CPS-TaskForge, TSP Dialogue Games): CPS-task environments are parameterizable (team size, modality, communication channel), instantiating diverse tasks such as tower-defense resource management or NP-hard graph tours (Haduong et al., 16 Aug 2024, Jeknic et al., 21 May 2025). Agents negotiate, share privileged information, and converge via dynamically updated state trackers and external optimization solvers.

These system designs accommodate scaling CPS from dyads to larger teams, heterogeneity in agent roles, multi-agent human–AI collaboration, and genre-specific task requirements.

4. Automated Diagnosis and Human–AI Complementarity

Recent advances enable scalable, explainable CPS diagnosis:

Transformer-based models: Both unimodal (BERT, RoBERTa, T5) and multimodal (AudiBERT) architectures outperform traditional RF/TF-IDF classifiers for CPS indicator detection across social-cognitive and affective dimensions (Wong et al., 21 Apr 2025, Wong et al., 19 Jul 2025, Zhu et al., 17 Jul 2024).
- AudiBERT delivers statistically significant improvements (W = 20.0, p = 0.031) for sparse social-cognitive classes but does not universally outperform BERT on affective indicators (Wong et al., 19 Jul 2025).
- Prompt-based PLMs achieve robust accuracy and macro F1 (>0.72) with minimal training data by leveraging label-word verbalizers and masked-token classification (Zhu et al., 17 Jul 2024).
Automated segmentation and transcription: Fully automated pipelines (Google ASR + VAD segmentation) yield near-oracle AUROC for CPS facet detection, despite granularity loss from under-segmentation (Venkatesha et al., 6 Jul 2025).
Human–AI workflow orchestration: Hybrid approaches maximize reliability, with human coders annotating high-consensus classes, AI models mass-labeling the remainder, and explainability modules (SHAP) highlighting contributory tokens. Model performance and explainability do not always coincide—spurious tokens (e.g., “alcohol”) may systematically bias class assignments (Wong et al., 19 Jul 2025).

These results support nuanced deployment: multimodality boosts detection of verbally-acoustic indicators, but not all CPS facets; human agency and model transparency remain indispensable for high-stakes educational and team performance assessment.

5. Group Composition, Internal States, and Diversity Effects

Sociocognitive and affective dynamics critically modulate CPS outcomes:

Demographic diversity: Group composition exerts a stronger influence on internal cohesion and social responsiveness than individual URM status; ethnically balanced teams show higher engagement metrics (e.g., social impact, responsivity) than majority-dominated groups (Cavazos et al., 7 Mar 2024).
Internal states: Retrospective self-reports synchronized with group video indicate CPS is dominated by positive affects (optimistic, engaged, satisfied), but confusion is both prevalent and may catalyze deeper engagement (Anindho et al., 3 Jul 2025).
Benchmarking collaboration: Nominal pairs—aggregate results from two individuals solving separately—serve as a critical baseline for evaluating whether true CPS exceeds the aggregated solo performance. In mixed-reality graph tasks, ad hoc pairs achieve higher accuracy (+4.6%) but take longer (1.46×) than individuals; however, performance is matched by nominal pairs, highlighting the importance of controlling for aggregation (Garkov et al., 19 Dec 2024).
Task complexity and process cost: Signal and noise instance complexities predict when collaboration leads to performance gains versus increased cognitive load; high clutter disproportionately disrupts paired coordination (Garkov et al., 19 Dec 2024).

Team assembly and process design must therefore balance diversity for cohesion, attend to latent emotional and cognitive dynamics, and employ rigorous benchmarks to validly assess collaboration gains.

6. Dataset Characterization and Guidelines for CPS Research

The empirical integrity of CPS analysis depends on dataset features, annotation standards, and analytical pipelines:

Multi-dimensional annotation: Datasets optimal for CPS research feature utterance-level coding for cognitive (PF, PU, ISA, DC, etc.), social (SU, BKG, TS, TA, IL, TL), and emotional (IEB, TEB) metrics, often using multi-label conventions (Villuri et al., 24 Dec 2024).
Existing dataset limitations: Most spoken language understanding corpora lack sufficient social and emotional coverage; multi-speaker interaction corpora (e.g., AMI) support better CPS modeling but still fail to capture open-ended, ill-defined problem solving (Villuri et al., 24 Dec 2024).
Design recommendations: New CPS datasets should incorporate 3–5 participant teams, mixed task types (well- and ill-defined), multimodal capture (audio, video, screen, physiological), role/floor tracking, and longitudinal sessions. Annotation guides should permit multi-function utterance labeling and standardize protocol for emotional and social signal coding (Villuri et al., 24 Dec 2024).

Adherence to these standards enables robust multi-task learning, graph-based team modeling, and adaptive, context-aware CPS agent deployment.

7. Implications, Best Practices, and Future Directions

Findings aggregate to actionable practices:

Platform features: Foster data/code visibility, reputation signals, tiered mentoring, threaded interaction spaces, and metadata standards (Hayashi et al., 2021).
Human–AI ensemble: Prioritize structured labeling workflows, model transparency, and fallback to expert judgment for ambiguous or low-consensus cases (Wong et al., 19 Jul 2025, Wong et al., 19 Jul 2025).
Group design: Assemble ethnically mixed teams for optimal sociocognitive dynamics, rotate assignments to avoid entrenched majority dominance, and implement protocols that scaffold equitable participation (Cavazos et al., 7 Mar 2024).
Task and environment tuning: Include aggregation-based controls (nominal groups), design for multisensory feedback, and dynamically allocate subtasks and scaffolds to align collaborative costs/benefits with task complexity (Garkov et al., 19 Dec 2024).
Dataset and tool curation: Release open benchmarks, pre-trained models, and evaluation scripts tailored for CPS classification, multi-label annotation, and group interaction graph mining (Villuri et al., 24 Dec 2024).

On-going research should further integrate multimodal signal fusion, adaptive agent orchestration, and cross-modal explainability into CPS systems, while continuously evaluating the interplay of platform design, team demographic composition, and algorithmic transparency in collaborative settings.