AI and Personalised Grading Criteria

Algorithmic Bias and Fairness

AI algorithms used for assessment and grading might inadvertently perpetuate the biases present in the data as they are trained on, leading towards unfair outcomes, particularly for marginalized groups. Addressing the biased algorithms and ensuring fairness in AI-grading systems is a crucial challenge. Ethical concerns surrounding AI use in higher education, highlight the need for responsible deployment, avoiding bias, and promoting transparency and accountability among stakeholders from various organizations (Slimi & Carballido, 2023). AI and learning analytics are revolutionizing personalized education in higher education. They enable personalized feedback and assessment, focusing on students' strengths and areas for improvement. However, ethical considerations, implementation challenges, and faculty training are crucial for successful adoption. This technology can enhance student engagement and optimize educational outcomes (Vashishth et al., 2024). The negative impacts of AI in higher education include biases, plagiarism, micromanagement, behavior manipulation, overreliance, and privacy concerns. It examines AI's impact on various processes, including student enrollment, employee hiring, teaching, research, and employee well-being. The paper also explores potential solutions and policy implications (Ivanov, 2023). AI advancements in mental health research challenge historical biases, highlighting the need for fair-aware AI development in psychological science to address health-equity implications (Timmons et al., 2023). The blurred lines between assessment and feedback in higher education, highlighting six issues: students' focus on grades, late feedback, subordination, documentation, and anonymous marking requirements, and proposes strategies for preserving feedback's learning function (Winstone & Boud, 2022).