Sp25 Conference Posters

Artificial intelligence (AI) is quickly becoming a key player in changing how students learn, especially in online courses. Use of AI is not only transforming how educators conduct assessments, but also provides opportunities to enhance learning.

The increasing prevalence of online learning has presented both opportunities and challenges for student engagement, retention, and academic performance. While distance education offers flexibility and accessibility, it also contributes to higher rates of isolation, disconnection, and decreased academic success. This poster presentation based on an article published in the Journal of Teaching and Learning with Technology, explores the role of positive psychology in fostering a sense of belonging and connection within virtual classrooms. By applying researched strategies, such as positive introductions, collaborative assignments, motor synchrony, and strong instructor-student relationships, educators can enhance student engagement and well-being.

The empirical framework of positive psychology, which focuses on human flourishing and optimal functioning, is essential in creating supportive learning environments. Research indicates that students who feel a sense of belonging perform better academically, experience lower anxiety, and are more likely to persist in their studies. The PERMA model—Positive Emotion, Engagement, Relationships, Meaning, and Accomplishment—provides a structured approach for integrating positive psychology into both virtual and in-person learning spaces.

Despite the benefits of online education, such as cost-effectiveness and broader accessibility, students enrolled exclusively in online courses face higher dropout rates and mental health challenges. Disparities in retention rates are especially pronounced among minority and low-income students. To mitigate these challenges, instructors must actively foster engagement, promote peer interaction, and provide meaningful feedback.

This research underscores the necessity of integrating positive psychology into virtual classrooms to improve student outcomes. By cultivating a welcoming atmosphere and fostering trust, educators can enhance motivation, participation, and resilience among students. Ultimately, the implementation of positive psychology strategies can bridge the gap between the benefits and drawbacks of online learning, ensuring a more inclusive and effective educational experience.

The Students as Partners framework is an inclusive and collaborative approach to education that actively involves students in shaping their learning experiences. By fostering mutual respect between students and faculty, this framework empowers students to contribute their perspectives, experiences, and insights to enhance educational practices.  In this project, a biology professor and a linguistics undergraduate student partnered to develop an AI literacy module for biology students. The module incorporates student perspectives on AI use, course policies, and ethical considerations, alongside biology-specific examples demonstrating how AI can enhance learning. By integrating both student and faculty viewpoints, the module addresses student concerns while providing discipline-specific context, creating a more well-rounded introduction to AI literacy.  This module will be implemented in a biology majors' seminar course in Fall 2025. By centering student voices in its development, we aim to foster a learning environment that is inclusive, engaging, and responsive to diverse student needs.

When we (the “experts”) write a solution to a workout/word problem, our solution is coherent, logical, well-executed, well-communicated, and correct. In short, we build an argument that our answer is correct and answers the question(s) asked. We demonstrate this ideal for our students (the “apprentices”) in class – often with lots of commentary – and encourage them to shoot for this ideal in their own solutions. Building to this ideal takes practice so solutions stand-alone, without commentary, and can be understood and followed by someone else.

To help the students practice and achieve this standard, I designed the “Comm Practice” assignments to help them develop their ability to effectively communicate their mathematical understanding while demonstrating their mathematical prowess. Across the progressive assignments, the students practice the type of communication that is expected on exams and get an opportunity to familiarize themselves with the grading rubric used to assess their work. Proverbial lightbulbs come on when the students exchange their Comm Practices in class and have to read/interpret/figure out someone else’s solution, and to do so with enough insight in order to award a grade using the provided rubric. These lightbulbs illuminate the need and value of the communication ideal, insights they regularly carry into their studies (helping them uncover misconceptions and/or procedural issues) and performance.

The Comm Practice assignments have been implemented in the past nine semesters of the US Air Force Academy’s “Calc 3” Multivariate Calculus course and regularly receive praise from instructors and students alike that these assignments are effective and are accomplishing what they were designed to do.  While anecdotal evidence is encouraging, this research uses data from three offerings of Calc 3 to formally assess the effectiveness of the Comm Practice assignments and to determine the most appropriate sequencing for the various Comm Practice forms.

We explored classroom dynamics in the post-COVID era by inquiring about student values of instructor traits and practices that influence the classroom culture at the U.S. Air Force Academy in 2025 (n = 140). We focused on classroom leadership traits rather than pedagogical technique, which often overlap. At the Academy, student developmental paths occur under both military and academic entities. The academic entity includes typical collegiate coursework taught by a combination of civilian and military instructors. We found that overall, the instructor’s degree of academic knowledge (distinguished from their degree of military knowledge), their ability to clearly communicate class content, and ability to inspire student participation were ranked highest. Conversely, the instructor clearly communicating military responsibilities, their professional etiquette, and providing constructive feedback pertaining to military bearing were ranked least, indicating that students found these elements less impactful to their learning experience. We also parsed student rankings by instructor type: civilian, military, and civilian that are prior active-duty military. Findings indicate that some techniques were ranked high across instructor types, while traits were ranked lower overall. Some elements of classroom leadership also varied slightly with the instructor type, and we discuss those. Our findings elucidate what students desire from faculty and subsequently may be more or less receptive to, which we frame by their influence upon classroom dynamics. The views expressed in this document are those of the authors and do not reflect the official policy or position of the U. S. Air Force, Department of Defense, or the U. S. Govt. IRB: FAC20250008E

As AI reshapes the educational landscape, first-year computing courses must adapt to emphasize not just coding skills but also deeper comprehension and synthesis of code. This poster explores two complementary approaches to improving introductory computing education: short-response concept questions and AI-augmented code interviews.  

The first approach uses short answer responses to multiple-choice and true/false concept questions to assess students’ understanding of fundamental principles. By shifting the focus to natural language responses, this approach helps to identify misconceptions and an AI-centric backend provides rapid feedback, reinforcing core computing knowledge.  

The second approach extends traditional coding assessments by incorporating AI-generated code alongside student-submitted code in an AI-augmented code interview process. While student-led code interviews are a well-established practice, the innovative addition of AI-generated code challenges students to engage in code comprehension and explanation. Rather than solely debugging or modifying their own work, students analyze, describe, and critique unfamiliar AI-generated code. This method cultivates critical reading skills, enhances problem-solving abilities, and prepares students for “vibe coding”: real-world, modern software development, where understanding generated code is often more important than writing new code from scratch.  

By integrating concept questions and AI-augmented interviews, this approach fosters deeper learning, metacognition, and digital literacy, equipping students with skills essential for the evolving technological landscape. The poster will present the code architecture, implementation plan, and preliminary prototype development results, highlighting how these methods can integrate into entry level computing courses. Additionally, it will explore the broader implications for computing education in the AI era, particularly in fostering deeper learning, metacognition, and digital literacy.