Investigating algorithmic bias in student progress monitoring

Idowu, Jamiu Adekunle; Koshiyama, Adriano Soares; Treleaven, Philip

doi:10.1016/j.caeai.2024.100267

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2024

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Cited by 3 publications

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Latent space bias mitigation for predicting at-risk students

Al-Zawqari,

Peumans,

Vandersteen

2024

Computers and Education: Artificial Intelligence

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Latent space bias mitigation for predicting at-risk students

Al-Zawqari,

Peumans,

Vandersteen

2024

Computers and Education: Artificial Intelligence

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A Framework for the Responsible Integration of Generative AI Tools in Learning

Ko,

Chan

2024

Advances in Educational Technologies and Instructional Design

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Generative artificial intelligence (Gen-AI) tools are increasingly utilized in educational settings for tasks ranging from content creation to personalizing learning experiences. While these tools offer considerable potential to transform education, their integration brings challenges, including biases, dependency, and ethical dilemmas. Addressing these challenges and ethical concerns is essential to fully leveraging Gen-AI to promote equitable and effective learning. This chapter presents a framework for the responsible use of Gen-AI tools in learning environments, offering guidance to educators, technologists, policymakers, students, and other stakeholders. The framework provides proactive guidelines to navigate the complexities of Gen-AI, ensuring these tools are employed ethically and effectively to enhance student learning outcomes.

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Integrating AI Into Policy Making in Educational Leadership

Mitchell

2024

Advances in Educational Technologies and Instructional Design

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Artificial Intelligence (AI) is revolutionizing various sectors, and its integration into education is becoming increasingly pivotal. This chapter, “Integrating AI into Policy Making in Educational Leadership,” examines the transformative potential of AI in shaping educational policies. The use of AI in educational leadership offers opportunities to enhance decision-making by making it more informed, efficient, and adaptive. Drawing on recent research, the chapter discusses how AI can impact key areas such as student motivation, engagement, anxiety reduction, and performance prediction. Additionally, it addresses the challenges and ethical considerations associated with implementing AI in policy-making processes. Through a comprehensive exploration of these themes, the chapter provides strategic insights for educational leaders seeking to leverage AI to improve outcomes and equity in education.

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Investigating algorithmic bias in student progress monitoring

Cited by 3 publications

References 18 publications

Latent space bias mitigation for predicting at-risk students

Latent space bias mitigation for predicting at-risk students

A Framework for the Responsible Integration of Generative AI Tools in Learning

Integrating AI Into Policy Making in Educational Leadership

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