๐Ÿง  AI Ed Wiki

๐Ÿ“„ Research Article

Artificial Intelligence and Collaborative Learning: Impacts on Creativity, Critical Thinking, and Problem-Solving

โ— high ยท 2026-08-11collaborative-learning, generative-ai, higher-ed, critical-thinking, problem-solving, creativity, mixed-methods

Synthesis: Artificial Intelligence and Collaborative Learning: Impacts on Creativity, Critical Thinking, and Problem-Solving

Key Findings

  • Mixed-methods study of 102 undergraduate students in interdisciplinary project-based courses, of which 96 responses (94.1%) were valid for analysis; qualitative input came from 80 open-ended survey responses and 15 semi-structured interviews, triangulated with analysis of student project outputs.
  • 83% of respondents reported that LLMs enhanced creativity (generating new ideas, offering different perspectives during brainstorming), and 78% said LLMs increased their group's overall productivity โ€” although 20% felt LLM use decreased individual accountability and contribution, creating workload imbalances.
  • 81% noted LLMs acted as neutral mediators that helped resolve differences and speed consensus building, while 17% indicated the presence of LLMs diminished the need for critical engagement, with AI suggestions sometimes accepted without discussion.
  • Correlation analysis showed the strongest relationship between consensus-building impact and productivity impact (r = 0.71), followed by collective intelligence and productivity (0.69); AI comfort correlated with perceived creativity impact (0.56) and problem-solving impact (0.59), and digital proficiency showed only moderate correlations (0.52, 0.47, 0.50).
  • No statistically significant differences in perceived LLM impact were found across groups based on prior AI use, AI comfort, or digital proficiency โ€” a t-test (t = 1.414, p = 0.2929) and ANOVA (f = 5.33, p = 0.1028) at ฮฑ = 0.05 both failed to reject the null hypothesis.
  • Respondents were predominantly business and management majors (62%), aged 18โ€“26 with an even gender split; 90% were very comfortable with AI tools and 96% had prior LLM experience, with digital proficiency rated intermediate (54%) or advanced (40%).

    • Study Design & Method

    The study used a descriptive, mixed-methods design with no variable manipulation. Quantitative data came from a structured Likert-scale survey measuring perceived impacts of LLMs on group collaboration, creativity, and problem-solving efficacy. Qualitative data came from open-ended survey items, audio-recorded semi-structured interviews transcribed for thematic analysis, and inspection of student project outputs for originality and collaborative innovation. Statistical analysis combined a Pearson correlation matrix with t-tests and ANOVA (implemented in Python) to compare perceptions across groups defined by AI comfort, digital proficiency, and prior LLM use. Because the hypothesis tests returned non-significant results, the authors additionally explored alternative factors (specific courses taken, exposure to AI tools, learning styles) and proposed k-means clustering as a way to segment student interaction patterns.

    Key Results

  • Perceived benefits: students credited LLMs with improving problem-solving (e.g., quick summaries of long texts freed groups to focus on higher-order tasks), structuring group discussion, synthesizing individual contributions into cohesive action plans, and boosting collective intelligence.
  • Perceived risks: overreliance on AI to build consensus was flagged as a threat to interpersonal skill development; some group members became passive and deferred to AI-generated solutions; instructors were advised to monitor group dynamics and intervene when workload distribution becomes imbalanced.
  • Digital competence does not shape perceptions: the absence of significant group differences suggests familiarity and digital skill are weak predictors of how students perceive LLM effects on creativity, problem-solving, consensus building, and productivity.
  • Balanced integration: the authors warn that heavy dependence on generative AI can impair self-regulated learning, intrinsic motivation, and performance, citing the phenomenon of "metacognitive laziness" โ€” outsourcing cognitive effort to AI reduces engagement with deeper learning strategies and can erode independent analysis, synthesis, and evaluation over time.

    • Implications for AI in Education

    The study's central message is that LLMs must be designed and deployed to support rather than disrupt human collaboration. Positive correlations between AI comfort and perceived creativity/problem-solving gains suggest that building familiarity with AI tools matters more than raw technical skill, so educators should teach strategic use alongside technical proficiency. The documented overreliance risks point to concrete design responses: group projects that require students to reflect on AI outputs and justify decisions independently of the AI's suggestions, clear guidelines on tool use, and instructor monitoring of group dynamics. The authors call for balanced AI integration that preserves the essential human elements of group work โ€” meaningful discussion, challenging assumptions, and collective knowledge construction โ€” connecting to Collaborative Learning and Critical Thinking research, and echoing Self Regulated Learning concerns that outsourcing cognitive effort ("metacognitive laziness") undermines deep learning. Findings also support Generative AI adoption in Higher Ed settings as a complement to, not a replacement for, human effort in teamwork.

    Connected Concepts

  • Higher Ed
  • Collaborative Learning
  • Critical Thinking
  • Self Regulated Learning
  • Generative AI
  • Socratic Method
  • Math Education
  • Creativity

    • Connected Articles

  • AI Assisted Collaborative Learning Model Dbr โ€” Design-Based Research for Developing an AI-Assisted Collaborative Learning Model to Enhance Critical Thinking and Problem-Solving Skills in Higher Education
  • LLM Critical Thinking Teamwork Review โ€” Can Large Language Models Foster Critical Thinking, Teamwork, and Problem-Solving Skills in Higher Education?: A Literature Review
  • Ccct Cooperative Learning Technique โ€” Artificial intelligence assisted design of a novel cooperative learning technique for higher education
  • Polished Artifacts Fragile Engagement 2026 โ€” Polished Artifacts, Fragile Engagement? Tackling the Challenge of Reduced Epistemic Effort in Human-AI Knowledge Construction
  • Chatgpt Critical Creative Thinking Review โ€” ChatGPT Critical and Creative Thinking: Systematic Review
  • AI Collaborative Learning Systematic Review โ€” A systematic review of AI-powered collaborative learning in higher education: Trends and outcomes from the last decade

    • Citation

    Agnaou, A., & El Asri, H. (2025). Artificial intelligence and collaborative learning: Impacts on creativity, critical thinking, and problem-solving.