TBR AI EXCHANGE

AI Learning Collaborative

AI-Assisted Learning in College Algebra

Submission Date

Submitter’s Name/Email

  • Bridgett
  • Smith
  • brsmith@southwest.tn.edu

Institution/School

  • Southwest Tennessee Community College (SWTCC)

Department/Discipline

  • Discipline-Specific Faculty – STEM & Technical Fields

Activity Purpose (assessment, data collection, classroom management, etc.)

  • Teaching

AI Tool(s)

  • Artificial intelligence tools, including Microsoft Copilot and generative AI platforms

Activity Details

AI-Assisted Learning in College Algebra

Context

Students enrolled in College Algebra often enter the course with varying levels of mathematical preparation. Many students struggle with foundational concepts, experience anxiety when solving multi-step problems, and require more individualized feedback than can reasonably be provided during class time. The goal was to increase student engagement, improve conceptual understanding, and provide timely support while promoting independent learning.

Application

Artificial intelligence tools, including Microsoft Copilot and generative AI platforms, were integrated into course activities to supplement instruction and provide personalized learning support.

Students were taught how to use AI responsibly to:

  • Generate step-by-step explanations of algebraic concepts.
  • Receive immediate feedback on practice problems.
  • Explore multiple solution methods for the same problem.
  • Create personalized study guides and practice questions.
  • Analyze errors in completed work and identify misconceptions.

AI was incorporated into classroom demonstrations, homework support activities, and guided learning exercises. Students were encouraged to evaluate AI-generated responses critically, verify mathematical accuracy, and explain the reasoning behind solutions rather than simply accepting answers.

Outcomes

The implementation produced several positive outcomes:

  • Students reported greater confidence when attempting complex algebraic problems because they had access to immediate explanations and examples.
  • Learners engaged more frequently with course material outside of class through AI-assisted practice.
  • Students demonstrated improved ability to identify and correct errors in their mathematical reasoning.
  • AI-supported feedback reduced frustration and increased persistence when solving challenging problems.
  • The experience reinforced responsible AI literacy skills, including critical evaluation of AI-generated content and appropriate use of technology in academic settings.

Lessons Learned

A key lesson was that AI is most effective when used as a learning partner rather than an answer generator. Structured guidance and clear expectations are essential to ensure students use AI to deepen understanding rather than bypass the learning process. Additionally, teaching students how to verify AI-generated responses helped strengthen critical thinking and mathematical reasoning skills.

Scalability

This case can be easily adapted for developmental mathematics, statistics, quantitative reasoning, and other STEM disciplines. The approach requires minimal resources, aligns with AI literacy competencies, and provides a practical model for responsible AI integration across the TBR system.

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