Supporting Students Through Finals: AI-Enhanced Strategies for Faculty at the End of the Semester
Authors: Kimberly Martinez; info@ciddl.org
As the semester comes to a close, a familiar pattern begins to emerge. Students who have demonstrated understanding throughout the term begin to stall. Final papers go untouched. Study plans never materialize. Faculty often interpret this as disengagement. In many cases, the issue is cognitive overload.
I have seen this firsthand, a student who participated actively all semester suddenly goes quiet in the final weeks, not because they stopped caring, but because the finish line feels overwhelming. That moment stuck with me. It is not always about the content. Sometimes it is about the cognitive weight of getting to the end.
Finals require planning, organization, and sustained attention. These demands often surface executive functioning challenges, particularly for students with disabilities. At the same time, faculty face increased grading and administrative responsibilities. Research indicates that educators spend substantial time on non-instructional tasks, which can limit their capacity to focus on instruction and student support (Goldman et al., 2024). As Goldman et al. (2024) note, artificial intelligence has the potential to “streamline time-intensive responsibilities,” allowing educators to redirect effort toward meaningful teaching and interaction.
This moment creates an opportunity to use artificial intelligence as a scaffold rather than a shortcut.
Break Finals Into Manageable Steps
Students often struggle to begin large, complex assignments. The barrier is not always understanding the task, but organizing how to start. Faculty can use AI to generate structured supports such as task breakdowns, timelines, and guided planning prompts.
These supports reduce cognitive overload and help students move from intention to action. Structured instructional approaches that clearly organize content and reduce unnecessary complexity have demonstrated strong effects on student outcomes. (Welker et al., 2025) emphasize that well-designed instructional supports can improve “knowledge, application, and maintenance” of learning. The same principle applies during finals. Clarity supports completion.
Strengthen Active Review
Many students rely on passive strategies during finals, such as rereading notes or reviewing slides. These approaches rarely promote deep learning.
AI can help faculty quickly generate targeted review materials. For example, a prompt like "Generate five application-based questions for an undergraduate course on special education law, focused on how IDEA applies to IEP development" can produce ready-to-use materials in seconds. Faculty can build on outputs like these to design review experiences that require students to apply, explain, and reflect, rather than simply recall.
This approach supports:
- application-based practice questions
- summaries aligned with learning objectives
- scenario-based prompts
- guided reflection activities
CIDDL’s blog “Boost Your Finals Prep with Artificial Intelligence” highlights how these tools can support more effective study practices. The goal is not to hand students answers, but to give them better ways to engage with what they already know.
Support Faculty While Sustaining Effective Practice
Finals week also increases demands on faculty. AI can assist with drafting feedback, generating comment banks, and identifying patterns in student work. These supports help maintain timely, meaningful feedback without overwhelming instructors.
CIDDL’s broader collection of AI-focused blogs emphasizes that technology should support, not replace, instructor expertise. This framing remains critical during finals, when both student need and faculty workload are at their highest.
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