Assessing faculty self-perceived knowledge in using generative AI to teach 21st-century skills

Synthesis: Sutedjo, Chowdhury & Liu (2026) survey 127 faculty members at a large U.S. research university, using the validated TPACK-21 instrument adapted for generative AI, to map self-perceived knowledge for teaching 21st-century skills with GenAI. They find strong traditional knowledge (pedagogical, content, and pedagogical content knowledge) but a pronounced deficit in the technology-integrated domains — technological pedagogical knowledge (TPK), technological content knowledge (TCK), and holistic TPACK — with overall TPACK the lowest of the seven domains.

Key Findings

• Traditional knowledge is strong, technology-integrated knowledge is not. Faculty reported high self-perceived pedagogical content knowledge (M = 4.70) and near-ceiling content knowledge (CK, M = 5.15), but markedly lower technological pedagogical knowledge (TPK, M = 2.62), technological content knowledge (TCK, M = 2.75), and holistic TPACK (M = 2.55, the lowest domain). Faculty feel like subject-matter experts yet perceive a real gap in knowing how to use GenAI to teach critical thinking, problem solving, Creativity, and collaboration.
• Content expertise does not transfer to GenAI integration. CK showed no significant correlation with TK or any technology-integrated domain (r = .11–.15, all ns). Disciplinary expertise did not predict GenAI-related self-perceived knowledge, indicating that GenAI knowledge is a distinct developmental need — professional development should not assume subject-matter mastery will carry over.
• The technology-integrated domains may collapse into one factor. TPK, TCK, and holistic TPACK correlated very strongly with each other (r = .81–.91) with near-ceiling internal consistencies (α = .97–.98), raising a discriminant-validity question: faculty in this sample may not distinguish among the three technology-integrated constructs, which could function empirically as a single GenAI-integration factor.
• Technological knowledge as a possible gateway. TK correlated strongly with the technology-integrated cluster (r = .67–.76), consistent with the interpretation that technological knowledge may function as a gateway through which integrated GenAI teaching capacity develops — though the cross-sectional design cannot establish direction.
• Modest gender differences. Male faculty reported significantly higher self-perceived TK and TPK (and TCK at the p = .05 threshold) than female faculty, all small effects (η² = .03–.04). No significant differences emerged by age group or career track across any domain.

Synthesis

The study extends the TPACK framework — originally developed for pre-service teachers — to practicing higher-education faculty using the TPACK-21 extension focused on 21st-century skills. Its distinctive contributions are two structurally important correlation patterns: content knowledge is decoupled from GenAI-integration knowledge, and the three technology-integrated domains may not be empirically distinct in this population. Both findings carry direct implications for faculty development: GenAI competence must be deliberately built through discipline-specific programming rather than assumed from subject-matter expertise, and the technology-integrated domains are so tightly interrelated that faculty development should treat them as a shared GenAI-literacy foundation rather than train them separately.

Connected Concepts

• Technological Pedagogical Content Knowledge (TPACK)
• Higher Education
• Generative AI
• Educational Development
• Self-Efficacy
• Teaching
• AI Literacy

Connected Articles

• From Proficiency to Pedagogy: A Mixed-Methods Study of In-Service Teachers' TPACK-GenAI and the Mediating Role of Pedagogical Knowledge — In-service teachers' TPACK-GenAI and the mediating role of pedagogical knowledge (Mohebi & ElSayary 2026)
• Designing faculty standards for technology integration in higher education institutions: a design-based research study — Faculty standards for technology integration (TPACK-related DBR)
• Efficacy of an Intensive Generative AI Professional Development Program on Pedagogical Content Knowledge (AI-PCK) and the Comparative Analysis of Learning Gain between Experienced and Pre-service Teachers — Intensive GenAI professional development and AI-PCK gains
• Evaluation Indicator System for AI Certificate Programs — Evaluation Indicator System for AI Certificate Programs

Citation

Sutedjo, A., Chowdhury, M., & Liu, S. P. (2026). Assessing faculty self-perceived knowledge in using generative AI to teach 21st-century skills. Frontiers in Education, 11, 1913310.