Assessment of influencing factors of college and universities’ teaching effects using fuzzy and deep learning techniques

AI assessment tools use artificial intelligence to evaluate student work, identify learning gaps, and generate insights for teachers. Examples include AI-powered essay feedback tools (Turnitin Feedback Studio), adaptive diagnostic assessments (NWEA MAP, Renaissance), automated short-answer grading, and early-alert dashboards that flag at-risk students based on engagement patterns.

For multiple-choice and structured short-answer assessments, AI grading achieves near-human accuracy. For essays and complex writing, AI feedback tools are most accurate on surface features (grammar, structure, citations) and less reliable on evaluating argument quality, creative originality, or cultural context. Most implementations use AI feedback alongside human review for high-stakes assessments.

Key concerns include algorithmic bias in grading tools that may disadvantage students from underrepresented groups, lack of transparency in how AI arrives at scores, privacy implications of detailed performance tracking, and the risk that over-reliance on AI metrics narrows what schools measure and therefore what they value in education.

Teachers use AI assessment dashboards to identify which concepts most students have not mastered, spot individual students who are struggling before problems escalate, tailor re-teaching to specific gaps rather than reteaching entire units, and allocate small-group support time efficiently by prioritizing students most likely to benefit from targeted intervention.