Organizer's Email: kerittby@peace.edu
Artificial intelligence tools are reshaping mathematics pedagogy, requiring educators to rethink assessment practices, curriculum design, and classroom instruction. This special session explores innovative applications of AI in teaching college mathematics across multiple dimensions. Topics include developing new assessment methods that thoughtfully integrate student use of generative AI while maintaining mathematical rigor, creating curricular materials that foster critical thinking and mathematical reasoning in an AI-augmented learning environment, and practical classroom demonstrations of AI tools that enhance student understanding of mathematical concepts. We invite presentations from faculty exploring both opportunities and challenges in integrating AI into mathematics education, sharing evidence-based approaches, and discussing strategies for preparing students to work effectively and ethically with AI technologies.
Presentation Time: 15 minutes Discussion/Transition Time: 5 minutes Total Time: 20 minutes
CONFERENCE INFORMATION: 105th Annual MAA-SE Section Meeting Dates: March 26-28, 2026 Location: University of North Alabama Session Date & Time: Friday, March 27, 2:00-3:40 PM OR Saturday, March 28, 9:15-11:15 AM
IF YOU ARE INTERESTED, PLEASE: Click New Submission and provide the following information: Your name and institutional affiliation Title of your presentation Abstract (100 words) describing the content of your talk Other notes or information about what you plan to share
College Algebra is a gateway course that presents significant challenges, such as varying student preparedness and math anxiety. This session explores the potential of ALEKS, an adaptive learning platform, integrated with artificial intelligence (AI), to enhance student engagement and learning outcomes. By examining classroom implementation and data-driven practices, this presentation demonstrates how ALEKS offers personalized learning paths, real-time diagnostic insights, and efficient remediation. It will also address AI's role in adaptive sequencing, feedback, and supporting equitable instruction. Participants will leave with actionable strategies for leveraging ALEKS to foster deeper mathematical understanding in College Algebra while aligning with desired learning outcomes.
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In this classroom action study, we compare an alternate assessment method to traditional exams in order to determine if there is a less stress-inducing way to assess students' understanding of the concepts in an undergraduate college algebra course. Grades on both alternate assessments and traditional exams, as well as the students’ perception of the assessment we're collected from three different sections of the course. This presentation will share the alternative assessment design, the methodology of the study, and the analysis of student data.
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This is a preliminary report on a study investigating the effectiveness of self-grading Excel spreadsheets as a feedback tool in STEM education, particularly focusing on their impact on student learning outcomes, engagement, and satisfaction. By providing students with instant feedback on assignments, these self-grading spreadsheets are intended to enhance students’ understanding and mastery of mathematical concepts. The study gathers student feedback to explore their perceptions of how these tools influence their learning process, confidence, and comprehension in mathematical contexts. Giving students the tools they need to develop confidence is critical to their self-efficacy and performance. Additionally, this research identifies and addresses the challenges of designing and implementing self-grading assignments, offering insights into best practices for integrating technology-driven feedback tools in STEM education. Preliminary findings suggest that self-grading spreadsheets may serve as a valuable resource in promoting active learning, with implications for improving student engagement and satisfaction. Student quotes on the positive effects for their learning from the assignments, obtained via in-semester surveys and end-of-semester course evaluations, will be shared. References Blayney P., Freeman M. (2004). Automated formative feedback and summative assessment using individualised spreadsheet assignments. Australasian Journal of Educational Technology, 20(2), 209–231. https://doi.org/10.14742/ajet.1360 Kovačić Z., Green J.S. (2012). Automatic grading of spreadsheet and database skills. Journal of Information Technology Education Innovations in Practice, 11, 53–70. Laing G., Kirkham R., Kampen T. V. (2020). An Automated Assessment Marking Approach: Using Excel to Grade an Accounting Practice Assignment. e-Journal of Business Education & Scholarship of Teaching, 14(3), 12-24. Mays T. (2015). Using spreadsheets to develop applied skills in a business math course: Student feedback and perceived learning. Spreadsheets in Education, 8(3). Fyfe, E. R., & Rittle-Johnson, B. (2016). The Benefits of Computer-Generated Feedback for Mathematics Problem Solving. Grantee Submission, 147. https://doi.org/10.1016/j.jecp.2016.03.009 Kangaslampi, R., Asikainen, H., & Virtanen, V. (2022). Students’ Perceptions of Self-Assessment and Their Approaches to Learning in University Mathematics. LUMAT: International Journal on Math, Science and Technology Education, 10(1), 1–22. McCarron K.B., Park T., Ellis Y. (2023). Intermediate accounting students’ reaction to Excel® homework assignments with a feedback (self-check answer) function. Journal of Instructional Pedagogies, 28. LoSchiavo F.M. (2016). How to Create Automatically Graded Spreadsheets for Statistics Courses. Teaching of Psychology, 43(2), 147-152. DOI: 10.1177/0098628316636293.
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What if, instead of trying to prevent students from using AI, we designed assessments that require collaboration with it? At a small liberal arts college, mathematics assessment for non-majors was reimagined through alternative grading and structured revision cycles. When assessment shifts from point accumulation to iterative feedback and proficiency-based revision, students begin to use AI not to produce answers, but to ask better questions, test ideas, and clarify their thinking. This session explores the evolving partnerships between students, instructors, and AI in redefined learning spaces, striving to increase a sense of belonging. Let’s explore redesigning assessment to sustain rigor, preserve human connection, and invite authentic mathematical curiosity in the age of generative AI.
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We will discuss how the remedial math course at our university was converted to mastery based assessment. This will include how we decided on the learning targets, how we handle the logistics of additional attempts, and what is mastery graded and what is not. Initial reactions from student surveys will also be included.
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It is well known that one big issue students have in math classes is related to solving word problems. So, what can we do in a class like Quantitative reasoning or Mathematical Modeling, where most of the problems they have to solve are word problems? How many examples could an instructor show their students to make sure they understand how to approach such problems? This presentation will look into how I use the AI in my Quantitative Reasoning classes as a tool that will help my students understand the process of solving word problems, creating examples and understanding the mathematical logic while applying it in real life. I will talk about what I call Unit Workshops, where my students work in groups to discuss different methods, they find using the AI and compare them to what we worked in class before, looking for errors that may affect the results and/or interpretation of the results. All these workshops are guided, and each group must write a report.
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The use of AI in the professor's office can be a source of great fun or great aggravation. This presentation shares specific, prosaic problems that were solved well by generative AI. In particular, we will explore three distinct success stories: 1. Administrative efficiency: building LaTeX files for class notes; 2. Assessment design: using AI to generate diverse, standards-aligned exam questions; and 3. Programmatic visualization: using AI-assisted coding to build Mathematica animations of poles of Padé approximants. The presentation will also candidly address the dead ends of generative AI, discussing failed attempts at posing research-level open problems and the frustrations of iterative assignment design.
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Standards-Based Grading (SBG) has many potential benefits for students, since it gives them multiple opportunities to demonstrate their understanding and doesn’t penalize taking longer to learn something. However, since most students have only experienced points-based grading in math courses, they are often wary of and confused by SBG. In this presentation, I’ll discuss how I introduce SBG on the first day of class, using small group discussions to help them identify issues with traditional grading and understand the benefits of SBG. This presentation is intended to be useful both for those already using SBG and those interested in learning more about it.
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As teachers, we are always looking for ways to work smarter, not harder, and AI can assist with that. In this talk, we look at a collection of ideas from AI used in calculus and introduction to proof classes. These ideas include some nuts and bolts topics like creating review sheets with solutions, writing creative word problems with useful diagrams, and generating a large collection of possible exam questions to choose from. We will also look at some more artistic ideas, like developing an image for the class's Canvas tile, generating playing cards about historical mathematicians, and creating a slide show about departmental graduates.
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