NOTE: Learning Stack sessions are approximately 15-20 minutes (depending on how much time is allotted for Q&A). There are two presentations per 1-hour time slot.
All sessions take place in Butler-Carlton Hall on the Missouri S&T campus
2-121-1 // Data Visualizations for Exploring the Writing Process and Author Collaborations
TYPE: Learning Stack Session (15-20 minutes)
Presenters:
Shahrez Yousefi – University of Missouri-St. Louis
Dr. Badri Adhikari – Associate Professor of Computer Science; University of Missouri-St. Louis
Audience: Higher Education
Time and Location: 12:15 – 1:00 p.m.; Room 121
This presentation explores techniques for visualizing the writing process and mapping author collaborations using contemporary data visualization methods. It highlights examples where writing process data, such as authorship, keystrokes, drafts, and revisions, are transformed into visual representations that reveal writers’ cognitive activities and collaborative dynamics. Strategies for categorizing raw input data based on the type, source, and location of changes within a document are introduced. Additionally, the presentation demonstrates multi-level visualization approaches that capture writing activity at the character, sentence, paragraph, and document scales, offering broader, context-aware insights into the writing process.
2-124-1 // Use of AI in the instruction of EE2800 – Electrical Circuits for non-believers
TYPE: Learning Stack Session (15-20 minutes)
Presenter:
Dr. BJ Shrestha – Associate Teaching Professor of Electrical and Computer Engineering; Missouri S&T
Audience: Higher Education; K-12 Education
Time and Location: 12:15 – 1:00 p.m.; Room 124
EE2800 – Electrical Circuits is an out-of-department survey course required for graduation for various non-Electrical/non-Computer Engineering majors. This course is often viewed as an unnecessary burden by most of these students and so, it needs extra motivation for these students to be excited about it. This presentation is about exploring the use of AI in the instruction of this course to provide such a motivation.
3-121-1 // Rethinking Authorship: A Situated Accountability Approach to Teaching AI-Assisted Writing
TYPE: Learning Stack Session (15-20 minutes)
Presenter:
Dr. Ryan Cheek – Assistant Professor of English and Technical Communication; Missouri S&T
Audience: Higher Education; K-12 Education
Time and Location: 1:15 – 2:00 p.m.; Room 121
As generative AI becomes embedded in student writing, traditional ideas of authorship no longer reflect how texts are produced. This session offers a concise introduction to situated accountability, an approach that helps students document, justify, and critically assess their use of AI tools. Grounded in critiques of legacy authorship models (Hosseini, Resnik, & Holmes, 2023) and scholarship on algorithmic bias and opacity (Noble, 2018; O’Neil, 2016), the session provides quick, classroom-ready techniques for guiding responsible and transparent writing in AI-integrated learning environments.
3-124-1 // What works in creating AI-resistant writing assessments.
TYPE: Learning Stack Session (15-20 minutes)
Presenter:
Dr. James Newman – Associate Professor of Political Science, Philosophy, and Religion; Southeast Missouri State University
Audience: Higher Education; K-12 Education
Time and Location: 1:15 – 2:00 p.m.; Room 124
There are searches AI cannot do. I have created a few assignments where I ask AI to make choices from a data set that AI is not able to make a value judgement in determining what to include.
4-124-1 // The ChemE Game Project: A Student-Driven Learning Approach
TYPE: Learning Stack Session (15-20 minutes)
Presenter:
Dr. Mahmoud Moharam – Assistant Teaching Professor of Chemical and Biochemical Engineering; Missouri S&T
Audience: Higher Education
Time and Location: 2:15 – 3:00 p.m.; Room 124
This presentation introduces The ChemE Game Project: A Student-Driven Learning Approach, an initiative that applies gamification principles to chemical engineering education. The presentation will show idea development and task management approach used. Key outcomes from students’ survey at end of the semester will be summarized and presented.