2026 Survey Insight Report: Use of AI in Team-Based Learning

Artificial intelligence continues to reshape teaching and learning, raising important questions about how educators can use it effectively without weakening the collaboration, reasoning, and accountability central to Team-Based Learning (TBL). InteDashboard’s 2026 Use of AI in TBL Survey builds on the findings collected in previous years. It examines changes in AI adoption, teaching effectiveness, student learning, workflow efficiency, institutional support, and professional development needs. New questions were also introduced to explore AI’s perceived impact on student collaboration and to provide greater context on respondents’ geographical and disciplinary backgrounds.

The survey received 63 responses across several regions and disciplines. However, the sample was weighted toward North America and healthcare-related fields, which should be considered when interpreting the findings. Open-text responses were categorized with the assistance of ChatGPT and subsequently reviewed manually to preserve accuracy, nuance, and context.

Key findings

• Education (Greater clarity on AI’s role): TBL remains highly valued, while declining uncertainty suggests educators are forming clearer views of AI’s impact on teaching and learning.
• Efficiency (More tangible workflow benefits): AI is becoming more embedded in teaching workflows, with more respondents reporting reduced effort in developing and implementing TBL activities.
• Experimentation (Broader applications across TBL): Educators are extending AI use across assessment, content creation, student support, feedback, pre-class preparation, and Application Exercises.
• Evolution (Toward responsible student use): More educators are allowing students to use AI, signalling a shift from excluding it toward structured use that preserves reasoning, collaboration, and accountability.

Challenges

Ethics, educator training, uncertainty about learning impact, and data privacy remained the leading barriers. Technical integration became less prominent, while insufficient funding and concerns about inaccurate AI-generated content increased. Although institutional support improved slightly, many educators still lack clear direction and practical guidance.

Conclusion

The 2026 findings suggest that AI use in TBL is moving beyond experimentation toward more deliberate implementation. Educators are reporting clearer learning impacts, broader use cases, and greater efficiency, while concerns around reliability, ethics, and student collaboration remain unresolved.

To translate growing adoption into meaningful learning gains, institutions should:

1. Equip educators with practical guidance on selecting, applying, and evaluating AI tools in TBL

2. Set clear expectations for responsible use, including accuracy checks, privacy, and academic integrity

3. Build students’ AI literacy so they can question outputs rather than accept them uncritically

4. Design AI-enabled activities carefully to preserve teamwork, discussion, accountability, and higher-order thinking

Download our white paper now to discover a deeper analysis of respondents’ experiences and actionable recommendations to help institutions navigate this transformation responsibly.