Advancing AI Education Outcomes through the Teach-Test-Test-Test (T4) Pedagogical Framework: An Empirical Study

Paper ID : 1061-ICEEM2025 (R1)

Authors

Wael Badawy * Eru

Abstract

Artificial Intelligence ( AI ) education is rapidly evolving, yet many instructional approaches fail to balance theoretical knowledge with iterative performance feedback. This study introduces and evaluates an enhanced pedagogical framework—Teach-Test-Test-Test ( T4 )—applied within undergraduate AI courses at the Egyptian Russian University (ERU). The T4 framework emphasizes initial guided instruction followed by three structured assessment cycles, enabling students to refine skills incrementally through continuous feedback. A quasi-experimental design was employed, involving 126 participants across three AI-related courses. Performance data were analyzed using repeated measures ANOVA, with post-hoc tests controlling for potential carryover effects between assessments. Results indicate statistically significant performance gains between the first and final assessments (p < 0.01 ), with notable variations across courses. The study contributes to AI pedagogy by providing empirical evidence that structured repeated testing can enhance retention, problem-solving accuracy, and conceptual understanding. Implications for AI curriculum design, instructional policy, and adaptive assessment strategies are discussed.

Keywords

Artificial Intelligence Education, T4 Framework, Iterative Assessment, Pedagogical Innovation, Egyptian Russian University.

Status: Accepted