Assessment and Enhancement of Artificial Intelligence-Based Technological Pedagogical Content Knowledge (AI-TPACK) Among English Language Teachers

The rapid expansion of Large Language Models (LLMs) and generative artificial intelligence technologies has transformed the prevailing paradigm of foreign language instruction, necessitating a revision of professional competency models for teachers. This study aimed to assess and enhance “Artificial Intelligence-based Technological Pedagogical Content Knowledge” (AI-TPACK) among English as a Foreign Language (EFL) teachers, using a quasi-experimental, single-group pretest-posttest design. The statistical population consisted of English teachers working in language institutes, from which ۳۰ participants were selected via purposive sampling. They participated in a ۴-week interactive intervention workshop (totaling ۱۲ hours of training) focused on pedagogical prompt engineering and the design of language tasks aligned with the CEFR framework. Data were collected using a validated ۲۴-item AI-TPACK self-efficacy questionnaire (based on a ۵-point Likert scale) and an analytical rubric for assessing lesson plan quality. Statistical analysis of the data using a Paired Samples t-test in SPSS ۲۶ revealed that the participants’ mean scores in all components Technological Knowledge (AI-TK), Pedagogical Knowledge (AI-PK), Content Knowledge (AI-CK), and integrated AI-TPACK competency showed a statistically significant increase following the intervention (p<۰.۰۰۱). Furthermore, the effect size calculated based on Cohen’s d index (d=۱.۸۹) confirmed the structural effectiveness of the intervention. The results of this study emphasize the necessity of revising teacher training curricula and shifting from a passive approach toward active pedagogical agency in the application of artificial intelligence.

کلیدواژه ها:

Generative Artificial Intelligence ، AI-TPACK Framework ، English Language Teacher Training ، Prompt Engineering ، Teacher Professional Development