Actionable companion · Learning design & outcomes
Learning with agency: Open Book Exams and Flexible Grading Systems: Post-COVID University Policies from a Student Perspective
AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.
Evidence and context
Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Higher education. Verify the participant population and task before transferring the findings.
Original authors’ abstract, reproduced under the linked source license:
Abstract Due to COVID-19, many universities have started offering real time video or recorded courses. This situation raises concerns about a decline in students’ learning outcomes and issues of unfairness regarding students’ exams and grade evaluations. Korean universities have introduced online open book testing in courses and a flexible pass grading system that allows students to select their final grades to improve fairness. This research investigates students’ thoughts and reasons for the test and the system through a questionnaire with 109 respondents and statistical methods such as nonparametric tests, multinomial regression and text-mining. Many students supported both the testing and the grading system, presenting balanced viewpoints by comparing their cons and pros. This finding suggests that these policies could be helpful in enhancing fairness in grade evaluation, relieving students of the learning burden and increasing their satisfaction. This study offers the implications that universities must develop standardized exam formats as well as various learning options in a rapidly changing situation with educational innovation (e.g., hyperscale and generative artificial intelligence). Keywords: course management, educational innovation, university management, education service, higher education
What kind of evidence is this?
Supporting AI learning science · Survey / observational study
Stage: Higher education. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.
Study methods · source passage
Drawing on a review of the literature concerning open book exams and grading systems after the outbreak of COVID-19, this study designed a survey to explore students’ opinions related to the open book exam and the flexible pass grading system and their reasons (Figure 1). Sample data were obtained after students completed their final exams in the form of open book exams within a real time and online environment.
Limitations and uncertainty · source passage
After completing the survey (120 pupils), the survey data of 11 students who did not completely answer the questionnaire were removed. The final response rate was 90.83% (=109/120 students). Descriptive statistics show that 109 students participated in the questionnaire and the majority (70.6%) expressed approval for the open book exam. Many students offered positive reviews (i.e., excellence at 67%) for the exam with a real time and online format. More than 63% of students favored the flexible pass grading system; in addition, looking at the demographic variables of the respondents (Table 1: Panel A and B), many students majored in business administration (81.7%). Some differences were detected among the respondents with regard to the six classes, and most respondents (98.2%) were Korean. The respondents for the first and second semesters were also relatively equal. Although this research does not show the correlation table of all variables due to length limitations, the linear relationship between variables seems to be relatively low. This research selected the variable of “semester” as a control variable for multinomial logistic regression as the number of datasets in it is larger than that of the factor of “course number”.
Design the system before the pilot
Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.
- Specify a measurable learning objective, a comparator and a task without tool assistance before adopting a system.
A 90-day district pilot
- Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
- Days 16–30 · Design. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. Name an accountable owner and set the baseline, success threshold and budget before implementation.
- Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
- Days 61–90 · Decide. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.
The board decision brief
Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.
Attribution and reuse
Dongsuk Kang. Open Book Exams and Flexible Grading Systems: Post-COVID University Policies from a Student Perspective. Behavioral Sciences (2023-07-21). https://doi.org/10.3390/bs13070607
Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.