Hybrid E-Assessment in Higher Education: Semi-Automated Grading of Paper-Based Written Examinations
arXiv:2606.08855v1 Announce Type: new Abstract: This paper examines the limitations of fully digital and partially digital e-assessment approaches in summative examinations in higher education. The analysis focuses on the didactic narrowing caused by closed question formats and on organizational, technical, and legal constraints that become particularly relevant in large student cohorts. As an alternative, the paper proposes a hybrid e-assessment approach that retains paper-based, problem-oriented examination tasks while enabling semi-automated grading. Assessment-relevant intermediate results
The increasing pressure for efficiency in higher education, combined with advancements in AI for text analysis and computer vision, makes semi-automated grading a timely solution.
This development addresses a significant bottleneck in higher education assessment, enabling more robust and scalable evaluation methods while preserving pedagogical value.
Traditional entirely manual grading of paper-based assignments in large cohorts can be augmented by AI, allowing educators to focus on more complex assessment aspects and feedback.
- · Higher Education Institutions
- · EdTech Companies
- · Students
- · AI Software Developers
- · Traditional fully manual grading services
Increased efficiency and consistency in grading large-scale written examinations.
Potential for refined pedagogical approaches, as educators gain more time for course design and personalized student interaction.
Evolution of assessment design towards tasks better suited for hybrid grading, potentially influencing curriculum development.
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Read at arXiv cs.AI