2026 ASEE Annual Conference & Exposition

Instrument Validation for Measuring Teachers’ Practice and Trust in AI-Based Educational Technology

Presented at DSAI-Session 5: Student Perceptions, Trust, and AI Use in Coursework

Objective. The aim of this study is to validate a unified survey combining the intelligent-Technological Pedagogical Content Knowledge (i-TPACK) scale and the Trust in AI-based Technology in Education (TRAIT-Ed) scale and develop a brief version of the combined 52-item scale. The survey was administered to K–12 teachers (N=121) before and after a two-day professional development workshop on artificial intelligence (AI) integration. The unified instrument is designed to capture both teachers’ instructional competence (i-TPACK) and trust in adopting AI-based educational technologies (TRAIT-Ed) and examines changes in teachers’ confidence, competence, and trust in using AI-based educational tools. This approach offers a more holistic view of AI adoption in education.

Method. Confirmatory Factor Analysis (CFA) was conducted to evaluate the internal structure of the unified instrument. The Weighted Least Squares Mean Variance (WLSMV) estimator was used given the ordinal nature of the data (likert-type scale). Model fit was assessed using common fit indices including chi-square (χ2), comparative fit index (CFI), Tucker-Lewis index (TLI), root mean square error of approximation (RMSEA), and standardized root mean square residual (SRMR). Skewness and Kurtosis values were used to assess univariate normality. Internal consistency was assessed using Cronbach’s alpha (α) and McDonald's Omega (ω) coefficients. Convergent validity was assessed by examining Pearson correlations between composite scores on the i-TPACK and Trust subscales.

Results. For the original combined instrument, overall, the internal structure of the factor solution consisting of eight latent variables, including, five i-TPACK factors: Technological Pedagogical Content Knowledge (TPACK), Technological Knowledge (TK), Technological Content Knowledge (TCK), Technological Pedagogical Knowledge (TPK), Ethics; and three Trust factors: Working alongside AI to improve pedagogy (Pedagogy), Perceived Benefits of AI in an educational setting (Benefit), Reasons for not trusting AI diagnosis (Barrier), demonstrated appropriate model fit except for the SRMR index, based on post-survey data: χ²=1930.160 (p-value=0.000), df=1,246.000, χ²/df=1.55, RMSEA=0.079 (90% CI=0.072-0.086), SRMR=0.107, and CFI=0.975, TLI=0.974. All item factor loadings (λ) were significant (p<0.001), supporting convergent validity. Twenty-one items were removed for reasons including low variability, cross-loadings, relatively weak loading and multicollinearity. An improved fit was achieved with a reduced 31-item version of the unified scale demonstrating good fit: χ²=518.056 (p-value=0.000), df=413.000, χ²/df=1.25, RMSEA= 0.054 (90% CI=0.038-0.068), SRMR=0.052, and CFI=0.993, TLI=0.992.

Significance of results. Overall, findings support the validity and reliability of a unified and brief version of a refined 31-item instrument. The final version of the i-TPACK-Trust instrument offers a holistic approach to measuring teachers’ trust and competence for AI integration in K-12 education. Insights from data collected using the final instrument can guide the design of professional development training programs for teachers and AI-based Educational Technology designers on improving practice and trust in AI-based EdTech.

Keywords: professional development, k-12 teachers, AI-based EdTech, confirmatory factor analysis (3 to 10 keywords)

Authors

Margaret Wacera Gatongi Virginia Commonwealth University [biography]

Margaret Gatongi is a doctoral student in the Research Assessment and Evaluation concentration in the Department of Foundations of Education at Virginia Commonwealth University. Her research interest is multidisciplinary, spanning research methodology and program evaluation within the context of education, with a particular emphasis on STEM. Additionally, Gatongi is interested in studying the role of cultural immersion in education, especially how it can enhance STEM learning and foster global citizenship among students.

PhD student
Research, Assessment and Evaluation Concentration
Foundations of Education
Moe Debbagh Greene Virginia Commonwealth University [biography]

Dr. Moe Debbagh Greene earned his Ph.D. in Education with a focus on Curriculum, Culture and Change/Instructional Technology and his M.Ed. in Curriculum and Instruction with a concentration in Instructional Technology from Virginia Commonwealth University. Dr. Greene is a Fulbright recipient with a strong record of leadership in instructional technology, program management, accreditation, assessment, and curriculum development. His is an Assistant Professor at Virginia Commonwealth University. His research focuses on instructional technology, particularly the use and application of emerging technologies such as makerspaces, virtual reality, and artificial intelligence (AI) in teacher education. His work leverages advanced research methods, such as machine learning, data analytics, and natural language processing (NLP), to investigate how technology can support personalized learning and data-driven instructional decision-making, as well as how technological tools can be integrated in evidence-based and measurable ways to enhance student learning and teacher professional development.
Radhika Barua Virginia Commonwealth University [biography]

Dr. Radhika Barua is an Assistant Professor in the Department of Mechanical and Nuclear Engineering at Virginia Commonwealth University (VCU) and a faculty affiliate of the Institute for Sustainable Energy and Environment (ISEE). She leads the Advanced Magnetic Materials Processing (AM²P) Laboratory, where her research integrates advanced manufacturing, materials sustainability, and systems-level life-cycle thinking. Dr. Barua’s teaching and outreach efforts emphasize transdisciplinary approaches to engineering education that connect sustainability, ethics, and design thinking.

She is a co-developer of the micro-credential course “Sustainability: Transdisciplinary Life-Cycle Design Thinking,” which operationalizes the Engineering for One Planet (EOP) framework through experiential learning and modular credentialing. Her broader educational mission focuses on embedding sustainability competencies into engineering curricula without increasing credit loads, fostering collaboration across disciplines, and preparing students with industry-relevant, data-driven design and life-cycle assessment (LCA) skills.
John E Fife Virginia Commonwealth University
Ms. Elizabeth A. P. Denson Virginia Commonwealth University [biography]

Beth Denson is an educator and program manager with a background in science communication, media literacy, and data literacy. She has expanded AI literacy training opportunities to students, educators, and the general public in Richmond, VA. Her most prominent events include the inaugural AI Teacher Workshop, providing practical AI training to over 200 educators, and the AI/Computer Vision Summer Camp, a week-long high school summer program for RITS and AI Ready RVA. Prior to her work in AI literacy, Beth taught high school science and coached a Speech & Debate Team. She holds an M.A. in Education and a B.S. in Biological Sciences from Virginia Tech.

Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 21, 2026, and to all visitors after the conference ends on June 24, 2026

« View session