{"id":10456,"date":"2026-09-08T08:00:52","date_gmt":"2026-09-08T13:00:52","guid":{"rendered":"https:\/\/www.everylearnereverywhere.org\/?p=10456"},"modified":"2026-09-08T09:43:50","modified_gmt":"2026-09-08T14:43:50","slug":"what-a-faculty-student-collaboration-learned-building-an-ai-tutor-for-college-math","status":"publish","type":"post","link":"https:\/\/www.everylearnereverywhere.org\/es\/blog\/what-a-faculty-student-collaboration-learned-building-an-ai-tutor-for-college-math\/","title":{"rendered":"What a Faculty-Student Collaboration Learned Building an AI Tutor for College Math"},"content":{"rendered":"

In the summer of 2025, Dr. April Crenshaw stood in front of her MATH 1710 precalculus students at Chattanooga State Community College and shared the experience she had been having with new AI platforms. She had been experimenting with building a customized tool in ChatGPT to serve as a virtual learning assistant, which she was hoping to pilot that semester. But she\u2019d hit some roadblocks.<\/p>\n

Afterward, David Escalante Gonzalez, an electrical engineering student in her class, stayed behind to ask her questions about the project: What was she trying to accomplish? What had she tried so far? What roadblocks were getting in her way?<\/p>\n

Crenshaw explained that her goal was to train the large language model (LLM) on course content and design it so students would work through the problem step by step, rather than just receive the answers, just like a tutor in the campus math center might. She also wanted to collect usage data to continuously improve the learning assistant the more students used it. However, she was having trouble getting the tool to perform consistently.<\/p>\n

Escalante, who had always been interested in computers, offered to help. Together, they created a reliable AI learning assistant and were able to pilot it in class that same semester.<\/p>\n

The need for virtual tutoring<\/h2>\n

Crenshaw had never built an AI tool before, but she tackled the project because she knew students needed extra help, especially in online classes. The Mathematics Department at Chattanooga State Community College had just received a grant to reimagine co-requisite learning supports but had also eliminated an existing bridge course that helped many students improve algebra skill before taking precalculus. Crenshaw worried what the results of these changes might be.<\/p>\n

\u201cMy face-to-face students were going to be fine,\u201d she says. \u201cBut I believed my online students would need more support.\u201d<\/p>\n

Most of Crenshaw\u2019s students work and balance family responsibilities while attending school. Those priorities often made it difficult to attend office hours or meet with Math Center tutors during normal hours of operation.<\/p>\n

Crenshaw also knew students who need help most often have the hardest time asking for it. It requires trust to open up about being confused and needing support, and it can be more difficult to build that relationship with remote learners.<\/p>\n

\u201cIt\u2019s easier to reach out to somebody you\u2019re seeing in person four times a week,\u201d Crenshaw says.<\/p>\n

She hoped an AI tutor could offer a lower-stakes way for students to get the help they needed in the exact moment that they needed it.<\/p>\n

Building the AI learning assistant<\/h2>\n

Crenshaw began building the tool in ChatGPT, but she found it struggled with inconsistency in its answers. The learning assistant would work perfectly on one example, then break down on the next one, even when the examples were nearly identical. \u201cOne of the worst things I could do was to present this new tool to students and then have it give the wrong answer,\u201d says Crenshaw.<\/p>\n

Initially, she tried to solve this by writing and rewriting the prompts, but when Escalante looked at her work, he pointed out that the AI was behaving unpredictably because it was getting lost in multiple sets of instructions. Instead of a prompt that delivered all instructions at the same time, he explained, they needed to build a modular system that sent different instructions according to how the conversation was moving.<\/p>\n

Crenshaw says her student \u201cgave me homework that week.\u201d He had her transfer her work off the ChatGPT platform and instead use OpenAI\u2019s API to build their own platform. Then the pair sat down and used pencil and paper to block out exactly what they were trying to do.<\/p>\n

Ultimately, they built an architecture for the instructions that began with a foundational layer and then routed student requests into the appropriate instructional mode. For example, in different pathways called worked example, guided practice, and error analysis, the learning assistant offers three levels of support so students can seek help in different ways.<\/p>\n

\"Chart

Crenshaw, A., Burns, L., & Gonzalez, D. (2026). Scaffolding with Formative Feedback: A Deployable AI Tutoring Prompt System. In Wiley, D. (Ed.), The Pedagogical Promptbook: Enacting Evidence-Based Teaching and Instructional Design Practices with Generative AI. EdTech Books. https:\/\/doi.org\/10.59668\/2340.26763<\/p><\/div>\n