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How can AI be used to improve assessment procedures and practices in education?

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To expand CourseBox's market by improving the existing product.

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Travis Clapp, CEO and Co-Founder of CourseBox.

With a strong background in instructional design, Travis experienced firsthand the challenges of building high-quality educational content, which inspired him to create CourseBox.

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Use artificial intelligence technology to automate scoring and feedback for daily assessments, as well as adaptive assessments that tailor the learning experience to each student's needs, and far beyond the traditional assessment steps, inclusive and adaptable thereby improving the effectiveness and efficiency of assessments.(Mao et al., 2023)

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Automated assessment and feedback tools can significantly improve grading efficiency and help students learn effectively through instant feedback. By automating the grading process, AI can promote higher-quality student-teacher interactions. Early automated essay grading tools and more recent automated assessment tools have all aimed to achieve this goal. But as AI technology grows and matures, the new capabilities of generative AI tools and their powerful abilities to present text, audio, images and the human body(Lamb et al., 2022) have the potential to further improve efficiency, feedback quality, and the user experience(Swiecki et al., 2022).

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AI offers the ability to personalize and adapt learning and assessment in ways that traditional assessment cannot. It can provide tailored feedback and next steps based on students' abilities and performance levels. Advances in artificial intelligence (AI) technology have enabled researchers to develop applications that simulate teachers' knowledge and experience to provide personalized support to students(Pai et al., 2020). In IT field, Computerized adaptive testing (CAT) can effectively facilitate student assessment by dynamically selecting questions on the basis of learner knowledge and item difficulty(Yang et al., 2022).Additionally, AI assessment tools support increases student engagement and enhances the personalized learning process, ensuring that learning is more effective and adaptive to each individual's needs(Elmahdi et al., 2018).

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Despite the availability of adaptive tools such as Braille keyboards, speech-to-text conversion, color inversion, etc., barriers to the accessibility of digital materials and online learning still exist. Many existing AI tools and practices, such as Apple's Siri, Microsoft's Seeing AI, and Google's Knowledge Graph, have already provided some accessibility solutions. More revolutionary AI tools, such as OpenAI's ChatGPT4, will further address accessibility challenges, including access to visual and audio materials, mobility-restricted access, readability, discoverability, and communicability (Hunter, 2023). AI will continue to enhance the accessibility and inclusivity assessment of usability and user-friendliness to suit the needs of different learners with different individual circumstances(Maram Fahaad Almufareh et al., 2024).

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1.Bias in AI Algorithms: Because of the limitation of technology, current AI-driven assessment tools can inherit biases from the data they are trained on, leading to unfair grades and evaluations of student(Ntoutsi et al., 2020). This could perpetuate existing inequalities, particularly for students from underrepresented or marginalized groups​(Leavy, 2018).

2.Academic integrity issues: Artificial intelligence can be used in education for more than just assessment, raising new concerns about plagiarism and authenticity(Mijwil et al., 2023). Generative AI tools such as ChatGPT can create original content that may be indistinguishable from student-created work, making it difficult to ensure academic integrity(Uzun, 2023). It may even score higher on assessment tools of the same origin AI.

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Elmahdi, I., Al-Hattami, A., & Fawzi, H. (2018). Using Technology for Formative Assessment to Improve Students’ Learning. Turkish Online Journal of Educational Technology - TOJET, 17(2), 182–188. https://eric.ed.gov/?id=EJ1176157

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Hunter, H. Z., Sylvia Izzo. (2023, June 5). Guest Post — Accessibility Powered by AI: How Artificial Intelligence Can Help Universalize Access to Digital Content. The Scholarly Kitchen. https://scholarlykitchen.sspnet.org/2023/06/05/guest-post-accessibility-powered-by-ai-how-artificial-intelligence-can-help-universalize-access-to-digital-content/

Lamb, R., Neumann, K., & Linder, K. A. (2022). Real-time prediction of science student learning outcomes using machine learning classification of hemodynamics during virtual reality and online learning sessions. Computers and Education: Artificial Intelligence, 3, 100078. https://doi.org/10.1016/j.caeai.2022.100078

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Leavy, S. (2018). Gender Bias in Artificial Intelligence: the Need for Diversity and Gender Theory in Machine Learning. Proceedings of the 1st International Workshop on Gender Equality in Software Engineering - GE ’18. https://doi.org/10.1145/3195570.3195580

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Mao, J., Chen, B., & Juhong Christie Liu. (2023). Generative Artificial Intelligence in Education and Its Implications for Assessment. TechTrends, 68. https://doi.org/10.1007/s11528-023-00911-4

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Maram Fahaad Almufareh, Kausar, S., Humayun, M., & Samabia Tehsin. (2024). A Conceptual Model for Inclusive Technology: Advancing Disability Inclusion through Artificial Intelligence. Journal of Disability Research, 3(1). https://doi.org/10.57197/jdr-2023-0060

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Mijwil, M. M., Hiran, K. K., Doshi, R., Dadhich, M., Al-Mistarehi, A.-H., & Bala, I. (2023). ChatGPT and the Future of Academic Integrity in the Artificial Intelligence Era: A New Frontier. Al-Salam Journal for Engineering and Technology, 2(2), 116–127. https://doi.org/10.55145/ajest.2023.02.02.015

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Ntoutsi, E., Fafalios, P., Gadiraju, U., Iosifidis, V., Nejdl, W., Vidal, M., Ruggieri, S., Turini, F., Papadopoulos, S., Krasanakis, E., Kompatsiaris, I., Kinder‐Kurlanda, K., Wagner, C., Karimi, F., Fernandez, M., Alani, H., Berendt, B., Kruegel, T., Heinze, C., & Broelemann, K. (2020). Bias in Data‐driven Artificial Intelligence systems—An Introductory Survey. WIREs Data Mining and Knowledge Discovery, 10(3). https://doi.org/10.1002/widm.1356

Pai, K.-C., Kuo, B.-C., Liao, C.-H., & Liu, Y.-M. (2020). An application of Chinese dialogue-based intelligent tutoring system in remedial instruction for mathematics learning. Educational Psychology, 1–16. https://doi.org/10.1080/01443410.2020.1731427

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Swiecki, Z., Khosravi, H., Chen, G., Martinez-Maldonado, R., Lodge, J. M., Milligan, S., Selwyn, N., & Gašević, D. (2022). Assessment in the age of artificial intelligence. Computers and Education: Artificial Intelligence, 3(3), 100075. https://doi.org/10.1016/j.caeai.2022.100075

Uzun, L. (2023). ChatGPT and Academic Integrity Concerns: Detecting Artificial Intelligence Generated Content. Language Education and Technology, 3(1). https://www.langedutech.com/letjournal/index.php/let/article/view/49

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Yang, A. C. M., Flanagan, B., & Ogata, H. (2022). Adaptive formative assessment system based on computerized adaptive testing and the learning memory cycle for personalized learning. Computers and Education: Artificial Intelligence, 3, 100104. https://doi.org/10.1016/j.caeai.2022.100104

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