Research Trajectory
From Educational Technology to Learning Integrity
A narrative of how field-based research in EdTech integration evolved into a focused inquiry on learning integrity, process evidence, and generative AI.
While researching educational technology and teachers' ICT integration, I observed that access and outcomes did not necessarily reflect meaningful learning. This led me to question how learning is evaluated — and shifted my focus to learning integrity. I began prioritizing evidence that captures how students learn, engage, and demonstrate understanding throughout the learning process.
Foundations in Educational Technology
My early exposure to educational technology began during graduate studies and field-based research. During my master's degree, this interest became central to my thesis — examining how teachers were prepared to integrate technology into their classrooms using the TPACK framework.
I found that while teachers had strong pedagogical knowledge, their technological confidence and ability to integrate technology varied widely. This realization shifted my focus from mere adoption of educational technology to examining how it can be implemented to make learning processes visible and meaningful.
Teachers' TPACK is highly context-dependent — shaped by infrastructure access, institutional support, and professional development quality rather than individual capability alone.
Learning Integrity in the Age of Generative AI
With the widespread adoption of GenAI tools in education, a deeper challenge emerged:
How can educational technology be designed to ensure learning credibility without undermining academic integrity?
As I engaged in research on GenAI and academic integrity tools, I observed that most approaches prioritized final submissions — often missing the learning journey. This raised a fundamental question: whether the final submission can be the sole evidence of credible learning and academic integrity.
Learning Integrity as Process and Evidence
This led to my growing interest in learning integrity — specifically, whether learning activities, processes, and evidence genuinely reflect learners' understanding and engagement.
My recent research proposes a 3P Framework (Person, Process, Product) for academic integrity. This framework suggests that educational tools need to address all three dimensions to support fair academic integrity decisions and enhance student learning.
Person
Verifying that the work genuinely reflects the learner's own understanding and effort.
Process
Making the learning journey visible — capturing drafts, revisions, and engagement over time.
Product
Evaluating the final output in the context of the process and person behind it.
International Dissemination
I have shared this work through workshops and presentations in both national and international settings — including at ACE 2024 (Tokyo) and NELTA 2026 (Kathmandu). These experiences have further shaped my perspective on designing educational technology that supports reflection, enables formative feedback, and generates credible learning evidence.
Asian Conference on Education
The Process Matters: Academic Integrity in the Age of AI — live session on process-oriented feedback.
NELTA International Conference
Three Ps for Academic Integrity in the AI Era — poster presentation of the 3P Framework.