How AI Shaped Students’ Perceptions of Assessment within Online Learning Environments

This survey-based study examined students’ perspectives on assessment types, assessment strategies, feedback, and influence of artificial intelligence on assessment completion in online higher education courses. A total of 440 students completed the survey. The top three assessment types rated in terms of perceived learner effectiveness were case study analysis, writing assignments, and design projects. The top three assessment types rated in terms of perceived effectiveness were multiple attempts/submission, grading rubrics/criteria, and individual student assignments. Students with extensive online learning experience rated several assessment types – including quizzes, proctored exams, multimedia projects, and participation activities – less favorably. Most learners preferred text-based feedback and valued timely responses, whereas views on AI-generated feedback were mixed: some appreciated its immediacy, but others found it generic and less personal compared to instructor feedback. Students also expressed diverse opinions about using AI to complete assessments, citing both benefits (e.g. writing support, organization) and drawbacks (e.g. inaccurate information, reduced learning). Finally, concerns about AI use included ethical issues, fair grading, skill development, and the risk of penalties for inappropriate use.