DS102 Foundation and Tools for Business Analytics Adoption and Resistance to Generative AI

DS 102: Foundation and Tools for Business Analytics

Gen AI, Adoption, Resistance

Reflection on Instructional Change:

One practice from the ACUE Gen AI course that I implemented was designing an AI-inclusive exercise to help students learn how to code in Python by writing prompts using a generative AI tool. To implement this, I introduced basic Python concepts and explained how generative AI could support coding tasks for beginners. I reassured students that while they wouldn’t yet fully understand the generated code, they could focus on observing results and learning through exploration. I chose this approach to reduce the initial intimidation of learning Python and to spark curiosity by providing immediate, tangible outcomes.

This practice had a significant impact on student motivation and engagement. Students enjoyed the interactive nature of the exercise, with one remarking, “It’s like having a coding assistant—I don’t feel as stuck anymore.” However, some students were confused by references to libraries, worrying they were expected to know them already. This feedback highlighted the need for clearer communication about the exercise’s goals and the structure of future lessons.

The "Teaching with AI-Inclusive and AI-Resistant Learning Experiences" discussion significantly influenced my instructional approach, particularly in balancing creative AI tasks with traditional methods. This insight guided me to alternate between AI-inclusive assignments and AI-resistant assessments, such as coding exams conducted on paper without computer access, to ensure students develop both practical and analytical skills.

In my "Observe and Analyze Session," I realized the importance of scripting instructions for new tools to reduce student anxiety. This insight motivated me to develop more structured guidance for future classes. Additionally, I appreciated the feedback I received from Kristina on an assignment I had designed, which encouraged students to explore coding with the assistance of AI and reflect on the experience.

**Evidence of Instructional Change**
Instructions for an AI-inclusive assignment.	Student Reflections	ACUE Completion Badge
Instructions for an AI-Inclusive Assignment. For this assignment, students were required to download a publicly available dataset and generate a graph exclusively using AI-generated prompts, not by writing code themselves. I explained that creating the desired graph will likely require multiple interactions with the AI to refine the results. Students should submit the following: the final graph they produced, the full set of prompts they used and the AI’s responses, a brief reflection on the experience of using AI prompts for coding tasks. Besides, each student should reply to other two students, creating a discussion board.	Screenshot of a student's reflection in the discussion board. One of my students shared her reflection about the exercise. She mentioned that AI couldn't understand her request at first, so she had to adjust the prompt a few times. She believes it made coding faster, but she had to experiment with it.

Practice and Reflect Teaching and Learning with AI:

The AI ACUE course has transformed my teaching practices by highlighting the opportunities for enhancing student engagement through AI tools, while also addressing challenges like ensuring clarity in expectations and fostering critical thinking. For example, I incorporated generative AI to help students write Python code prompts, which boosted their confidence and motivation by enabling immediate, hands-on learning, even as I balanced this with traditional assessments to build foundational skills.

Course Syllabus

DS102_Syllabus_F24.pdf

**Faculty Bio**
Faculty Image	About me:
	Fernanda Maciel is a Professor of Business Analytics at California State University. She holds a Bachelor's degree in Statistics from the Federal University of Rio de Janeiro (UFRJ), an MBA in Marketing from the Pontifical Catholic University of Rio de Janeiro (PUC-Rio), and both a Master’s in Marketing Analytics and a Ph.D. in Business Analytics from Bentley University. Beyond academia, Fernanda shares educational content in statistics through social media and hosts the Estatisticast podcast, where she explores the practical applications of data analysis in various fields.

Funding
This work was supported by:	Logos
US Department of Education Hispanic Serving Institution Project TECH4Equity US Department of Education Hispanic Serving Institution Project STEM4Equity P031C210012 *Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or the US Department of Education.