A Study on Multimodal AI-Powered Outcome-Based Teaching Assessment System

DOI: https://doi.org/10.62517/jhet.202615208

Author(s)

Chaomin Gao1,2, Fang Liu1,*

Affiliation(s)

1School of Business Administration, Baise University, Baise, Guangxi, China 2The Revitalization and Development of Old Revolutionary Areas in Guangxi, Baise, Guangxi, China *Corresponding Author

Abstract

To address the core problems existing in the current Outcome-Based Education (OBE) teaching assessment in universities, such as simplistic assessment dimensions, delayed feedback, and ambiguous goal achievement levels, this study proposes a dynamic teaching assessment system driven by multimodal AI. By integrating multimodal data, the system establishes a dynamic mapping mechanism between teaching objectives and assessment content, realizing a closed-loop process from goal input to feedback improvement. Specifically, the system employs semantic analysis technology to decompose vague and abstract curriculum objectives into measurable, implementable, and evaluable behavioral indicators, and generates an intelligent question bank covering different cognitive levels based on the principle of "reverse design and forward implementation". Meanwhile, through a human-machine collaborative evaluation mechanism, it combines AI's automatic scoring of objective questions and structured answers with teachers' review and arbitration of open-ended answers, thereby improving assessment efficiency and reliability. Finally, an implementation path is proposed, starting from pilot applications in standardized courses, gradually expanding to practical courses, and ultimately establishing a university-wide teaching objective database, so as to provide infrastructure support for the digital transformation of education.

Keywords

Multimodal Artificial Intelligence; OBE Teaching Assessment; Human-Machine Collaborative Evaluation; Digital Transformation of Education

References

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