Despite strong evidence supporting the value of formative assessment for teaching and learning (Black & Wiliam, 1998), its implementation remains challenging. Studies show that the uptake of formative assessment practices is considerably influence by various factors, and its uptake is often difficult and superficial (Wang et al., 2021).  These challenges are commonly described in literature as stemming from both personal and contextual factors (Bennett, 2011). These personal and contextual challenges are compounded by a strong assessment culture and accountability pressures (Asian Development Bank, 2011; Simper et al., 2022). In Asian context, studies suggest that educators’ practices remain predominantly summative, with formative strategies often used superficially or inconsistently due to contextual constraints such as workload, time pressure, and limited professional support (Sulaiman et al., 2020).

In recent years, digital technologies and more recently artificial intelligence (AI) have been increasingly positioned as potential means to support formative assessment practices by addressing some of these long-standing challenges, such as providing timely feedback, supporting task design, and managing workload (Hopfenbeck et al., 2023). However, the integration of AI into formative assessment does not occur automatically, nor it is shortcut for learning but these new forms of learning are a catalyst for deeper learning (Chatfield, 2025). Educators’ decisions to adopt, adapt, or resist AI-driven formative assessment tools are shaped by their familiarity with AI, their confidence in using such technologies, and the broader institutional and ethical conditions within which they work. Despite growing interest in AI in higher education, there remains limited empirical evidence on how educators’ AI literacy competencies relate to the adoption of AI-driven formative assessment in practice. This study addresses this gap by investigating the relationship between higher education educators’ self-perceived AI literacy competency and their adoption of AI-driven formative assessment, and by exploring the factors that influence such adoption. Two research questions guide the study: (1) Is there a relationship between educators’ self-perceived AI literacy competency and their adoption of AI-driven formative assessment? and (2) What factors influence higher education educators’ adoption of AI-driven formative assessment tools?

The study is theoretically informed by Cultural-Historical Activity Theory (CHAT), which conceptualises learning and professional practice as socially situated and mediated by tools within activity systems (Engeström, 2001). From this perspective, AI is understood as a mediating tool whose adoption is shaped by interactions between subjects (educators), objects (formative assessment practices), rules (institutional policies and norms), community (colleagues and students), and division of labour (support structures and responsibilities). CHAT provides a useful lens for examining how educators’ familiarity with AI develops and how social, institutional, and ethical conditions influence decisions to confidently adopt or cautiously use AI in their formative assessment practices.

A mixed-methods research design was employed. Quantitative data were collected through an online survey administered to higher education educators in Malaysia (n = 84). The survey measured self-perceived AI literacy competency across four dimensions: knowledge, awareness, confidence, and usage/adoption. Statistical analyses, including correlation analysis, were conducted to examine relationships between AI literacy dimensions and the adoption of AI-driven formative assessment tools. Qualitative data were collected through semi-structured interviews with a purposive subsample of educators (n = 9). The interviews explored educators’ experiences with predictive and generative AI tools, their formative assessment practices, and perceived enabling and constraining factors influencing adoption. Interview data were analysed thematically.

Preliminary analysis suggests that educators who report higher levels of confidence and familiarity with AI tend to engage with AI-supported formative assessment in more deliberate and pedagogically purposeful ways, particularly in relation to feedback provision, task design, and supporting student learning, rather than adopting such tools for lesson planning. However, emerging findings indicate that AI literacy alone does not fully account for patterns of adoption. Instead, a range of contextual factors including institutional assessment policies, workload and time pressures, access to professional development, ethical considerations, and peer support appear to play a significant role in shaping educators’ decisions to adopt or cautiously adapt AI-driven formative assessment tools.

Asian Development Bank. (2011, November). Higher education across Asia: An overview of issues and strategies (Report). Asian Development Bank. Bennett, R. E. (2011). Formative assessment: a critical review. Assessment in Education: Principles, Policy & Practice, 18(1), 5–25. https://doi.org/10.1080/0969594X.2010.513678 Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education: Principles, Policy & Practice, 5(1), 7–74. https://doi.org/10.1080/0969595980050102 Chatfield, T. (2025). AI and the future of pedagogy (White paper). Sage. https://doi.org/10.4135/wp520172 Engeström, Y. (2001). Expansive Learning at Work: Toward an activity theoretical reconceptualization. Journal of Education and Work, 14(1), 133–156. https://doi.org/10.1080/13639080020028747 Hopfenbeck, T. N., Zhang, Z., Sun, S. Z., Robertson, P., & McGrane, J. A. (2023). Challenges and opportunities for classroom-based formative assessment and AI: A perspective article. Frontiers in Education, 8, 1270700. https://doi.org/10.3389/feduc.2023.1270700 Huesca, G., Elizondo-García, M. E., Aguayo-González, R., Aguayo-Hernández, C. H., González-Buenrostro, T., & Verdugo-Jasso, Y. A. (2025). Evaluating the Potential of Generative Artificial Intelligence to Innovate Feedback Processes. Education Sciences, 15(4), 505. https://doi.org/10.3390/educsci15040505 Simper, N., Mårtensson, K., Berry, A., & Maynard, N. (2022). Assessment cultures in higher education: reducing barriers and enabling change. Assessment & Evaluation in Higher Education, 47(7), 1016–1029. https://doi.org/10.1080/02602938.2021.1983770 Sulaiman, T., Kotamjani, S. S., Abdul Rahim, S. S., & Hakim, M. N. (2020). Malaysian public university lecturers’ perceptions and practices of formative and alternative assessments. International Journal of Learning, Teaching and Educational Research, 19(5), 379–394. https://doi.org/10.26803/ijlter.19.5.23 Wang, Y., Liu, C., & Tu, Y.-F. (2021). Factors Affecting the Adoption of AI-Based Applications in Higher Education: An Analysis of Teachers Perspectives Using Structural Equation Modeling. Educational Technology & Society, 24(3), 116–129. https://www.jstor.org/stable/27032860