Artificial Intelligence: The New Tool of Disruption in Educational Performance Assessment
Mahantesh Halagatti,
Soumya Gadag,
Shashidhar Mahantshetti,
Chetan V. Hiremath,
Dhanashree Tharkude and
Vinayak Banakar
A chapter in Smart Analytics, Artificial Intelligence and Sustainable Performance Management in a Global Digitalised Economy, 2023, vol. 110A, pp 261-287 from Emerald Group Publishing Limited
Abstract:
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.
Keywords: Academic performance measurement; artificial intelligence; disruptions; educational institutions; industry readiness; big data (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (text/html)
https://www.emerald.com/insight/content/doi/10.110 ... 9-37592023000110A014
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (application/pdf)
Access to full text is restricted to subscribers
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eme:csefzz:s1569-37592023000110a014
DOI: 10.1108/S1569-37592023000110A014
Access Statistics for this chapter
More chapters in Contemporary Studies in Economic and Financial Analysis from Emerald Group Publishing Limited
Bibliographic data for series maintained by Emerald Support ().