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Our contribution to ISATT's 40th Anniversary Yearbook focusing on Studying Teaching and Teacher Education grows out of our experiences across time in diverse Lands/Place, situations, and relationships. The knowledge we center have grown through relationships and experiences of great violence and harm alongside experiences and relationships where we have experienced abiding commitments to wholeness and healing. Our individual and collective attentiveness to the spiritual dimensions in the stories we live, tell, retell, and relive about striving to live in good ways, in ethically relational ways, has connected us over time. Living alongside and thinking with one another has shaped our movements beyond the colonial and human-centric understandings of stories of/as experience and thinking with stories that often dominate in (research for) teacher education and development. Attending the spiritual dimensions of stories of experience expands the educative potential of thinking with stories. As humans who are composing lives as educators on Indigenous lands where the colonial project continues to be genocidal for Indigenous peoples and Lands/Place, attentiveness to the spiritual dimensions of experience feels imperative if the next generations of children and youth in schools, and adults in teacher education and development, are to experience these places as educative and non-violent, and as opening potential to interrupt the pervasive colonial narratives that continue to dominate.

Introduction: Numerous decision-making situations are faced in education where Artificial Intelligence may be prevalent as a decision-making support tool to capture streams of learners’ behaviours.

Purpose: The purpose of the present study is to understand the role of AI in student performance assessment and explore the future role of AI in educational performance assessment.

Scope: The study tries to understand the adaptability of AI in the education sector for supporting the educator in automating assessment. It supports the educator to concentrate on core teaching-learning activities.

Objectives: To understand the AI adaption for educational assessment, the positives and negatives of confidential data collections, and challenges for implementation from the view of various stakeholders.

Methodology: The study is conceptual, and information has been collected from sources comprised of expert interactions, research publications, survey and Industry reports.

Findings: The use of AI in student performance assessment has helped in early predictions for the activities to be adopted by educators. Results of AI evaluations give the data that may be combined and understood to create visuals.

Research Implications: AI-based analytics helps in fast decision-making and adapting the teaching curriculum’s fast-changing industry needs. Students’ abilities, such as participation and resilience, and qualities, such as confidence and drive, may be appraised using AI assessment systems.

Theoretical Implication: Artificial intelligence-based evaluation gives instructors, students, and parents a continuous opinion on how students learn, the help they require, and their progress towards their learning objectives.