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Prior to the onset of the COVID-19 pandemic, women did nearly three-quarters of the world’s unpaid work. As institutional supports, including in-person school and community-based care for children, the elderly, and the disabled vanished early in the pandemic, many women’s caregiving responsibilities increased. In some cases, opportunities for paid employment disappeared due to layoffs and furloughs, while in others, paid work was no longer possible without access to the missing institutional supports. Either way, access to needed supports – financial, practical, and social – was diminished. The lapse of needed supports also had severe impacts on subgroups of women, including pregnant and post-partum women. A range of considerations – vaccine safety, social interaction and infection risk, disease severity – have posed serious challenges for pregnant and post-partum women. Across the board, women’s need for continuous access to better social, financial, and practical supports at home, in the community, and in the workplace was made even more evident by the COVID-19 pandemic.

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.