Status : Verified
| Personal Name | Orbeta, Tristan C. |
|---|---|
| Resource Title | Emergent transdisciplinary teacher preparation framework from generative Al-enriched biology instruction and pre-service teachers’ TLPACK, digital citizenship, and cognitive offloading |
| Date Issued | June 2025 |
| Abstract | This study developed a teacher preparation framework based on empirical evidence of the effects of transdisciplinary biology instruction with Generative Al on pre-service teachers’ Technology, Learner, Pedagogy, Content, and Context Knowledge (TLPACK), digital citizenship, and cognitive offloading. It also looked into the moderating effect of GenAl literacy on Biology instruction with Generative Al integration, and the interactions among TLPACK, digital citizenship, and cognitive offloading of pre-service teachers. A convergent parallel mixed methods research design was utilized, where the quantitative strand was a quasi-experiment involving a three-group repeated measures factorial configuration, while the qualitative strand was a multiple case study design. A total of 92 pre-service teachers (PSTs) from three comparable intact sections that were enrolled in a science education course in a state university were involved in the study. After random determination, one group was exposed to the intervention combining Universal Design for Learning, Generative Al, and Transdisciplinary Biology Instruction or UGTBI (n = 29). Another group was exposed to the intervention combining Generative Al and Transdisciplinary Biology Instruction or GTBI (n = 30), while the third group was considered as the comparison group (CBI, n = 33). Data on participants’ TLPACK, digital citizenship, and cognitive offloading were collected using researcher-developed instruments with acceptable reliability. Results of the one-way MANOVA, converging completely with the qualitative findings, showed that both UGTBI and GTBI were effective in improving pre-service teachers’ TLPACK and digital citizenship. On the other hand, results of the aligned rank transform (ART) ANOVA revealed increase in the participants’ cognitive offloading across seven learning cycles in favor of the intervention groups (UGTBI and GTBI). Structural Equation Modeling (SEM) revealed the following significant i |
| Degree Course | Doctor of Philosophy in Education (Biology Education) |
| Language | English |
| Keyword | biology education, biology instruction, Interdisciplinary approach in education, generative artificial intelligence in higher education, generative AI, artificial intelligence, biology teachers, TLPACK |
| Material Type | Thesis/Dissertation |
Preliminary Pages
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Category : F - Regular work, i.e., it has no patentable invention or creation, the author does not wish for personal publication, there is no confidential information.
Access Permission : Open Access