Conference for Advancing Evidence-Based Learning

Abstract:

The Writing Program Assessment Committee conducted a survey of Writing Program faculty addressing one of our core learning goals: “Students practice critical reading strategies.” Our analysis suggests no single definition of what critical reading is, but a sense of disruption in the context of AI. Faculty think of critical reading as slow, careful, close, rhetorically aware, contextualized, and analytical. Many see AI tools as a disruptor: students want reading to be less time-consuming, more accessible and digestible, and might see AI as a facilitator.  

This suggests a powerful inflection point for pedagogy. Core curricular goals need to be re-interrogated and/or redefined in the context of AI. This is already happening in real-time as our colleagues re-shape their reading lists, classroom activities, and syllabi. What is needed now is a clear vision of where we want to go as professors at an R1 institution.

Presenters:

Emily Avery-Miller
Teaching Professor
Writing Program, English Department
College of Social Sciences and Humanities 

Sarah Finn
Teaching Professor
Writing Program, English Department 
College of Social Sciences and Humanities

Tom Akbari
Part-Time Lecturer 
Writing Program, English Department
College of Social Sciences and Humanities

Matthew Noonan 
Associate Teaching Professor
Writing Program, English Department 
College of Social Sciences and Humanities

Fi Stewart-Taylor
Postdoctoral Teaching Associate
Writing Program, English Department
College of Social Sciences and Humanities

Abstract:

Deploying course-specific AI tutoring assistants across seven graduate data analytics courses, we expected to study whether AI tutoring improves learning outcomes. Instead, we encountered a more fundamental question: what happens when institutionally designed AI support meets students who already have established AI practices? Our Fall 2025 pilot (554 students, 37 sections) found that while 85.4% of surveyed students used AI-powered support tools during their courses, only 19.8% used the provided course-specific tutor. A parallel deployment in undergraduate English allows for cross-program comparison. This gap between general AI use and institutional tool adoption raises questions about student agency, AI literacy, and the role of instructors in learning environments where students arrive with existing AI fluencies. This Work-in-Progress presentation shares implementation lessons, explores why students may prefer general-purpose AI over course-trained alternatives, and examines what human-centered AI integration means when students arrive with AI relationships we did not design.

Presenters:

Cameron Sheehy
Associate Director
Supplemental Academic Services
College of Professional Studies 

Joseph Reilly
Assistant Teaching Professor
STEM Grad Programs
College of Professional Studies 

Sasha Goldman
Assistant Teaching Professor & Director 
Supplemental Academic Services
College of Professional Studies 

Abstract:

Participatory modeling (PM) is a collaborative approach to building and using models to understand, design and test solutions to complex problems, where desirable action is hard to define and coordinate. PM helps elicit diverse stakeholder knowledge and collaboratively produces novel and impactful solutions. The inherent difficulty of handling complexity in collaborative processes makes PM uncertain and open-ended, creating significant challenges in the training of future practitioners and researchers. We show how incorporating mindfulness, improvisational theater, and conceptualization of complexity cultivates the skills for successful PM and decision-making, through purposeful integration of stakeholders’ diverse values and knowledge in transparent and explainable modeling, and of playful approaches to collaborative exploration and solution-building. A qualitative analysis of students’ uptake of PM skills revealed that systematically cultivating mindfulness and improvisation techniques greatly enhanced the learning experience and confidence that students felt in their ability to co-design and facilitate successful PM processes. 

Presenters:

Moira Zellner
Professor
School for Public Policy and Urban Affairs 
College of Social Sciences and Humanities 

Antonia Sohns
Water Resources Specialist
World Bank 

Sebastian Ruf
Data Scientist
Industry

Abstract:

As artificial intelligence becomes embedded across disciplines, students need more than technical tools; they need strong foundations and critical thinking skills. This session shares an evidence-based teaching approach used in the MS Applied Mathematics and MS Statistics programs at Northeastern University that combines mathematical foundations with experiential, human-centered learning. Students first build core concepts in probability, statistics, linear algebra, and modeling, then apply these ideas through coding labs, teamwork, and real-world XN projects developed with industry partners. 

By understanding how AI methods work–not just how to run them–students learn to question results, interpret evidence, and make responsible decisions. The approach improves engagement, transfer of knowledge, and career readiness, preparing students for co-ops and jobs. Participants will leave with practical strategies to integrate foundations, authentic projects, and AI tools in ways that deepen learning and can be adapted across many disciplines.

Presenter:

He Wang
Associate Teaching Professor
Department of Mathematics
College of Science

Abstract:

Active student engagement is crucial for learning, yet many students hesitate to participate due to fear of embarrassment. The Instructor Mistake Meter (IMM) addresses this by rewarding students with bonus points for identifying instructor errors, aiming to increase engagement and foster psychological safety. This study examined the IMM’s effects across two semesters of undergraduate biomechanics. The experimental group (Fall 2025, n=67) used the IMM; the control group (Spring 2025, n=62) received equivalent bonus points alternatively. 

Experimental students asked 139% more questions than controls (p=0.001). Thematic analysis revealed six themes, with engagement (54%) and motivation (46%) most cited. Students reported increased attentiveness, comfort asking questions, and improved learning through error identification. However, quantitative surveys showed no significant confidence differences between groups. While feedback was mostly positive, some noted trivial errors were distracting. The IMM effectively increases classroom participation and may enhance psychological safety, though broader impacts require further investigation.

Presenter:

Daniel Grindle
Assistant Teaching Professor
Bioengineering
College of Engineering

Abstract:

The increasing presence of AI in higher education has disrupted traditional approaches to student learning and evaluation, and given rise to disconnects between and among students and instructors on perceptions of AI use. This project hopes to address these issues by connecting student and instructor perceptions of AI use (or non-use) to support specific learning goals, in light of shared ethical priorities. It develops a student-centered workshop where students collaboratively draw a flowchart that clarifies shared understandings of ethical priorities regarding AI and then traces how these can be applied to determine appropriate AI use (if any) for a particular learning goal.

Presenter:

Sara Morrell
Computational Social Science Research and Teaching Coordinator
Digital Integration Teaching Initiative
NULab for Digital Humanities and Computational Social Science
College of Social Sciences and Humanities

Abstract:

First-year seminars are a well-established practice for promoting students’ success and persistence during initial semesters of college. The John Martinson Honors Program’s first-year seminar, Honors Discovery, is designed to facilitate Honors students’ exploration of themselves as local and/or global citizens and their knowledge about the opportunities, resources, and financial support available to them.  

In Fall 2025, we transitioned Honors Discovery to a hybrid delivery model. Students attended monthly class sessions, complemented by online course materials and assignments outside of the traditional classroom. Our course evaluation indicates that students achieved learning outcomes at comparable rates to in-person delivery, and still enjoyed high rates of instructor effectiveness/satisfaction. Beyond leveraging online teaching methods, the course also adopted scaffolded AI use to support student engagement with their final assignment. Overall, this course delivery demonstrates how technological innovation does not necessarily entail the diminishment of student learning and sense of community in a learning experience.

Presenter:

Justin Silvestri
Associate Director of Learning & Curricular Engagement
John Martinson Honors Program

Abstract:

Healthcare professionals increasingly work in teams to provide the best care for their clients. As educators preparing future professionals, we recognized the need to give students authentic opportunities to practice collaboration across disciplines before entering the workforce. This presentation shares the design and implementation of a virtual interprofessional education (IPE) event that convened 132 graduate students from two different healthcare programs across two of Northeastern’s network campuses, Boston and Charlotte.  

Students prepared by watching introductory videos about the other profession and reviewing a shared client scenario. During the synchronous online event, students first met with same-discipline peers to discuss the case, then joined cross-disciplinary groups to develop collaborative care plans. Pre- and post-event surveys showed increased student confidence in teamwork, understanding professional roles, and recognizing the value of collaboration for client outcomes. We’ll share our five-step planning framework and lessons learned to help other educators create similar transformative learning experiences. 

Presenters:

Kimberly Ho
Assistant Clinical Professor
Communication Sciences and Disorders
Bouvé College of Health Sciences

Maeve Donnelly
Associate Clinical Professor, Director of Supervision for ABA Programs
Department of Applied Psychology
Bouvé College of Health Sciences

Sarah Young-Hong
Associate Clinical Professor, Undergraduate & Graduate Program Director
Communication Sciences and Disorders
Bouvé College of Health Sciences

Laura Dudley
Clinical Professor and Associate Chair of the Applied Psychology Department
Department of Applied Psychology
Bouvé College of Health Sciences

Abstract:

Evidence suggests students may increasingly use AI to bypass foundational learning, appearing to achieve course outcomes while missing core concepts essential for future success. This research examines a scaffolded approach to AI integration across a 12-week analytics course sequence. Students begin with limited AI use while building foundational understanding, then receive deliberate instruction on using AI for technical tasks, and finally learn to use AI as a collaborative tool that strengthens their own work rather than replacing their thinking. Data collection includes weekly reflections on learning experiences and periodic assessments of conceptual understanding. Preliminary observations suggest that explicit instruction leads to more thoughtful AI use, while indiscriminate use creates cumulative learning gaps. This presentation shares early patterns and discusses how educators across disciplines can adapt the principle of pairing expanded AI access with deliberate instruction.

Presenter:

Andrew Kinley
Assistant Teaching Faculty
MPS Analytics 
College of Professional Studies

Abstract:

Experiential learning is central to Northeastern’s identity, yet students often struggle to articulate what they have learned from experiences such as co-op, global programs, Service-Learning, or research. Participation alone does not guarantee learning. This Work-in-Progress explores how meaning-making can be intentionally designed as a bridge between experience and assessment. Drawing from ongoing work within the Honors Program, this session examines emerging approaches to structured reflection, timing, and light-touch assessment that support student sense-making without overburdening faculty or students. The presentation also considers the careful role AI might play as a scaffold for reflection rather than a shortcut for thinking. Rather than sharing finalized results, this session invites participants into an evolving design process focused on helping students translate experience into insight, transfer, and narrative learning that endures beyond the experience itself.

Presenter:

Seth Robertson
Associate Director of Honors Experiential Learning
University Honors Program

Abstract:

Faculty across disciplines face a common challenge: how to redesign assignments when students can use generative AI to produce polished work instantly. Many want to set clear AI expectations and preserve academic integrity but lack the time, frameworks, or starting point to act. The Assignment Redesign Assistant addresses this gap. Built as a template-driven AI project, it acts as a pedagogical consultant that guides faculty through a structured conversation: parsing assignment components into efficiency, analytical, and reflective task types, then generating a complete redesign aligned with the instructor’s chosen approach : AI-Resistant, AI-Integrated, or Process-Focused. Faculty can select their preferred detail level and iteratively refine the output in conversation. Each redesign includes learning objectives, transparent instructions, AI use guidance, and assessment criteria grounded in Backward Design, TILT, and UDL frameworks, aligned with institutional AI teaching policies.

Presenter:

Priyank Bagad
AI Instructional Assistant, CATLR
MS in Computer Software Engineering, ‘26
Khoury College of Computer Science

Abstract:

This poster presents an approach to undergraduate research training that transforms students from hesitant learners into confident researchers through iterative practice. In this advanced psychology laboratory course, students first participate as naive subjects in cognitive experiments, then transition to analyzing their own behavioral data, conducting statistical analyses, and writing scientific reports. Teaching two parallel sections enables solid datasets while creating authentic interdependence among students. The course employs three complete research cycles with structured revision opportunities, allowing students to master research skills through repetition and feedback. Students work with randomly assigned groups across projects, building collaboration skills and adaptability. By semester’s end, students deliver professional presentations celebrating their growth. This process-oriented model addresses a critical gap in higher education: bridging theoretical knowledge with hands-on research competency. The approach demonstrates how experiential learning, meaningful peer collaboration, and iterative revision can develop transferable analytical and communication skills essential across disciplines and careers.

Presenter:

Reyyan Bilge
Associate Teaching Professor
Department of Psychology
College of Science

Abstract:

As AI tools become increasingly present in academic settings, the opportunity lies in leveraging them intentionally to deepen learning rather than shortcut it. This poster presents two conversational AI initiatives developed at Northeastern University, each designed to scaffold deeper understanding through meaningful engagement. BodeBot is a chat-based AI study companion for ME 4555 at the College of Engineering, offering three pedagogically grounded modes including Socratic inquiry, Feynman-style concept articulation, and Notes Exploration, moving exam preparation beyond passive review toward genuine conceptual mastery. At D’Amore-McKim School of Business, the Case Study Personalization initiative tailors assignments to students’ professional backgrounds and career objectives, maintaining academic rigor while directly answering their fundamental question: “Why am I doing this work?” Together, these tools demonstrate that conversational AI, when designed with pedagogy at its center, transforms routine coursework and study into genuinely meaningful learning experiences.

Presenter:

Ritujit Chaudhury
AI Instructional Assistant, CATLR
MS Engineering Management, ‘27
College of Engineering

Abstract:

As educators increasingly encounter AI-driven change, there is an increasing need for a thoughtful partnership with technology that preserves the human touch. This poster responds by focusing on video as a practical, pedagogy-first approach that helps instructors engage students with and without advanced tools. Video can strengthen instructor presence, promote accessibility, and support genuine learning across modalities, offering a grounded entry point for faculty who may feel uncertain about rapid technological shifts. Anchored in research-based frameworks such as Mayer’s Cognitive Theory of Multimedia Learning, the session highlights strategies that foster connection, dialogue, and student agency.

Presenters:

Juli Charkes
EdD program alum (’23)
College of Professional Studies

Edie Magnus
Student
EdD Program
College of Professional Studies

Abstract:

The poster will discuss the use of service-learning in “Globalization and International Affairs,” the introductory course in International Affairs. It will be based on the experience of using service-learning over the previous years, and it will offer an overview of the process of creating local and global community partnerships. The presentation will also include examples of the specific service-learning activities students engaged in through these partnerships. The experiential component of service-learning enhances learning through hands-on engagement while the service component connects students to the community they are serving and enables them to have a positive social impact. The presentation will also include lessons learned from designing and implementing service-learning activities over several semesters.

Presenter:

Youly Diamanti-Karanou
Associate Teaching Professor and Undergraduate Program Director
International Affairs
College of Social Sciences and Humanities

Abstract:

Doctor of Nursing Practice (DNP) students must complete 1,000 supervised practice hours while demonstrating competency across numerous professional standards—a complex tracking challenge. This poster showcases an innovative AI-assisted tool that transforms how students monitor their progress and reflect on their learning.

The automated tracker uses Excel/Google Sheets to log practice hours across courses, while AI analyzes student-reported experiences to generate personalized narrative summaries and competency gap analyses. What might take faculty hours to compile manually, AI accomplishes in minutes, providing students with comprehensive visual and written feedback that prompts meaningful reflection on their professional development.

This project demonstrates practical AI integration that enhances rather than replaces human connection in education. The tool enables deeper student-faculty conversations focused on growth and learning. Attendees will explore a replicable framework for partnering with AI to create discipline-specific educational solutions that reduce anxiety, increase transparency, and support student autonomy across diverse programs.

Presenters:

Mary Lynn Fahey
Associate Clinical Professor
School of Nursing
Bouvé College of Health Sciences

Lisa Rinke
Assistant Clinical Professor
School of Nursing
Bouvé College of Health Sciences

Jackie Bertman
Senior Learning Experience (LX) Designer
Experiential Digital Global Education (EDGE)
Bouvé College of Health Sciences

Abstract:

This poster explores how undergraduate students navigate co-op decision-making, integrating survey data and faculty insights to reveal key emotional, strategic, and identity-driven factors. Attendees will learn practical strategies to support student confidence, reflective choices, and professional growth, informing advising practices and experiential learning curriculum.

Presenters:

Gaby Fiorenza-Hagopian
Assistant Co-op Coordinator
College of Social Sciences and Humanities

Becky Buchmelter
Associate Co-op Coordinator
College of Social Sciences and Humanities

Michaela Modica
Assistant Co-op Coordinator
College of Social Sciences and Humanities

Abstract:

How do we teach students to use AI responsibly when they’re already using it, often poorly or secretly? This poster shares a case study/lesson design from a graduate engineering career course where students learned to partner with Claude AI for job searching while maintaining critical thinking and authentic self-presentation.

Students used AI for resume editing, company research, and interview preparation through guided, hands-on activities. Critically, the lesson dedicated substantial time to AI’s limitations and when human judgment is irreplaceable. Rather than banning AI, the approach taught students to recognize when AI enhances versus undermines their work.

The presentation shares feedback and will engage attendees in collaborative discussion: What are your concerns about student AI use? What strategies work in your context? Together, we’ll explore teaching authentic AI partnership across disciplines.

Presenter:

Terri Gu
Assistant Co-op Coordinator & Assistant Director
MGEN
College of Engineering

Abstract:

This project explores how large language models (LLMs) can be used in accounting education to help future accountants maintain integrity when facing economic and social pressures. Building on research on accountants’ professional dilemmas, the project develops a LLM-based learning module that simulates realistic ethical and other professional dilemmas accountants encounter in practice. Through guided dialogue, the LLM helps students recognize pressure, reflect on conflicts of interest, and rehearse principled responses grounded in professional responsibility rather than short-term performance goals. The project evaluates whether such interactive simulations enhance ethical awareness, judgment consistency, and confidence in resisting inappropriate influence. The central aim is to position LLMs not as substitutes for judgment, but as educational tools that support reflection, independence, and professional integrity in accounting practice.

Presenter:

Frank Hartmann
Joseph M. Golemme Research Professor of Accounting
D’Amore-McKim School of Business

Abstract:

Inconsistent AI policies create a perfect storm: students anxious about crossing invisible lines, faculty overwhelmed by policy creation, and academic integrity offices flooded with ambiguous cases. This poster shares compelling national data showing explosive AI growth, from 66% to 92% of students in one year, alongside rising integrity concerns and critical literacy gaps. We introduce a practical framework for transparent AI policies grounded in recent research on policy effectiveness (Cullen & Murphy, 2025; McDonald et al., 2025; Weatherspoon, 2022). Drawing on EDGE’s implementation experience, we demonstrate how clear guidelines coordinated by a designated instructional leader transform institutional culture from confusion to competence. Attendees will learn to craft course-level AI policies using our four-component framework, understand the critical coordination role ensuring consistent messaging, and implement buy-in strategies. This session provides actionable templates, workflows, and evidence-based approaches institutions can adapt immediately.

Presenters:

Lauren Hatfield
Learning Design Manager
Experiential Digital Global Education (EDGE)

Sydney Johnson
Learning Experience Designer
Experiential Digital Global Education (EDGE)

Abstract:

In the age of AI and uncertainty, students seek ways to learn through human connection and relatable, real-world examples. In addition to hosting experienced speakers when possible, I advocate for “peer guest panels” – dedicated in-class panels featuring fellow undergraduate students or recent graduates. Peer speakers engage the class, for 20-25 minutes, with their unique business, leadership, entrepreneurship, management, or social impact roles and experiences, depending on the topic of that class. Speakers inspire the class to make relatable real-world connections to course concepts. I draw from social learning theory, social identity theory, and the identity-based learning literature to explain the multiple benefits of this accessible, cost-effective practice: Students in class learn through memorable experiences of peers who share central identities with them, while also expanding their social capital and access to opportunities; speakers benefit from a public speaking opportunity that also highlights the meaning of their work.

Presenter:

Ravit Heskiau
Associate Teaching Professor
Management and Organizational Development
D’Amore-McKim School of Business

Abstract:

The belief about one’s ability is a critical determinant of success and persistence in computer science. In this poster, we present a virtual learning environment developed for teaching introductory virtual reality programming. Our study findings indicate that, when controlling for spatial ability, students with less experience show higher gains in self-efficacy in the virtual reality. When controlling for experience, students with lower spatial skills show greater relative improvement in self-efficacy in virtual reality than those using an equivalent desktop application.

Presenter:

Wallace Lages
Assistant Professor
College of Arts, Media and Design and Khoury College

Abstract:

Laboratory Safety Compliance training is often perceived as dull, burdensome, and disconnected from real practice, yet its outcomes matter deeply. This poster shares a scenario-based, gamified approach to laboratory safety training that shifts learning from rule memorization to decision-making and consequence awareness. Learners are placed in a realistic “first day in the lab” simulation where they make choices, observe outcomes, and learn through guided reflection.

Developed within the constraints of a compliance-focused learning management system, this case demonstrates how experiential, action-driven learning can be designed and delivered. The session also explores the role of AI as a design collaborator and its future potential as a learning coach. Participants will leave with practical insights for designing human-centered, transferable learning experiences across disciplines.

Presenters:

Peggy Lei
Learning Design and Systems Support Specialist
Office of Academic and Research Safety

Jamie Tessler
Director of Global Safety Program Development & Outreach
Office of Academic and Research Safety 

Abstract:  

As GenAI reshapes creative industries, design students need computational thinking skills to prototype and evaluate complex systems. This poster explores how GenAI can accelerate learning by removing technical barriers to coding while maintaining critical human judgment. In an Algorithmic Graphic Design course, students use GenAI to create design tools and design visual identity systems, learning to balance GenAI capabilities with their own contextual expertise and cultural understanding. Through techniques such as step-back prompting and AI-assisted debugging, students develop both fluency in computational thinking and systems design, as well as critical AI literacy. Early observations suggest students engage more deeply with systems thinking when GenAI handles coding, allowing them to focus on conceptual frameworks and design decisions. This approach positions GenAI as an imperfect collaborator, one step removed from final outputs. GenAI helps students develop the adaptive capacities they need for an uncertain future where human creativity and judgment remain essential. 

Presenter:  

Todd Linkner 
Assistant Teaching Professor
Art + Design
College of Arts, Media, and Design

Abstract:  

We have created a new four-credit community-based experiential learning course ENGR 4956 through the Michael B. Silevitch and Claire J. Duggan Center for STEM Education in the College of Engineering. The course has no prerequisites and is designed to safely and responsibly engage Northeastern students in informal STEM outreach and education. The course fulfills the NUPath Experiential Learning (EX) graduation requirement, especially for students who are unable to complete a traditional co-op, which is relevant to the broader Northeastern community.  

After four consecutive semesters of offering this course as an independent study in a small cohort model, the students’ own ePortfolios, personal reflections, “ungraded” evidence, feedback survey responses, TRACE evaluations, and program evaluation data offer preliminary evidence that the course is meeting its original objectives of engaging Northeastern students and our nearby communities in informal STEM education and outreach that mutually benefits all partners. 

Presenters: 

Jennifer Love
Associate Teaching Professor & Associate Director of STEM 
College of Engineering

Claire Duggan
Executive Director
Michael B. Silevitch and Claire J. Duggan Center for STEM Education College of Engineering

Abstract:

This poster focuses on research into educational practices that use artificial intelligence (AI) as a learning tool to coach calculus students toward developing mathematical resilience. We explore how AI is reshaping education through a mathematics education lens, with particular attention to three pillars: resilience, coaching, and college readiness in calculus. The first pillar, resilience, examines how students engage with, critique, and experiment with AI to overcome challenges and barriers in calculus. The second pillar, coaching, highlights the role of the lecturer in prompting student engagement with AI through classroom activities and fostering meaningful participation. The third pillar, college readiness in calculus, emphasizes a deliberate balance between human connection—such as interactions with peers, lecturers, and tutors—and engagement with technology, including AI. Through implementing these three pillars, we reflect on a fundamental question: why learning matters and why we teach mathematics, as well as other subjects.

Presenter:

Matthew Meangru
Head of Mathematics
College of Science, Northeastern University-London

Abstract:

This poster reports on Design and Culture of Sustainability, a course-based research experience implemented at the College of Arts, Media and Design in Fall 2025, in partnership with NU Sustainability. Under the coordination of a faculty member, an interdisciplinary student team engaged in a range of research practices and developed a project investigating values and barriers to sustainable behavior among the student population and proposed strategies to promote sustainable practices on campus. Collecting information from more than 350 students, promoting focus group and co-design sessions, the research team generated findings and offered recommendations for sustainable initiatives for the NU Boston campus. This work increases student access to research opportunities by expanding possibilities for learning and practicing sustainability-related research and design competencies. In this presentation, we will share observations and practical tips to develop collaborative research with students, overcome challenges, and achieve important academic and developmental outcomes.

Presenter:

Najla Mouchrek
Associate Teaching Professor
Art + Design
College of Arts, Media and Design

Abstract:

This poster explores how knowledge graphs and AI can improve how students navigate their academic paths. I built a recommendation system for Massachusetts tourism that uses graph databases to connect over 57,000 locations through 156,000 relationships. When users ask questions like “what’s near Boston for families?” the AI traverses these connections to provide context-aware recommendations.

The same technical approach could help students plan their learning. Instead of simple keyword matching, a graph-based system could understand how courses relate through prerequisites, difficulty progressions, and conceptual connections. Students could ask “I want to learn data science but struggled with statistics, what makes sense?” and receive recommendations based on actual knowledge structures.

This poster presentation demonstrates the working travel system and invites educator feedback on whether this approach addresses real advising challenges and how to best model educational relationships.

Presenters:

Hotragn Pettugani
Graduate Student and Teaching Assistant
Information Systems
College of Engineering

Tirdesh Pettugani
MSIS Alumni and Previous Research Assistant
Information Systems
College of Engineering

Abstract:  

This study examined the impact of high-fidelity Code Blue simulation on confidence and perceived stress among pre-licensure nursing students. Students from multiple campuses completed surveys before and after participating in a standardized cardiac arrest scenario designed to mirror real clinical emergencies. The simulation emphasized clinical decision-making, communication, and teamwork within a safe, supportive environment. Quantitative survey data were analyzed to identify changes in self-confidence and stress related to responding to a Code Blue event. In addition to traditional analysis in Excel, artificial intelligence tools supported data organization, pattern recognition, and visualization, enabling efficient comparison of pre- and post-results across groups. Findings suggest that structured simulation experiences increase confidence and help students better manage stress associated with high-stakes clinical situations. This work highlights the value of combining human-centered experiential learning with AI-supported data analysis to strengthen educational research and inform evidence-based nursing education practices. 

Presenters:   

Kristin Stankard
Assistant Teaching Professor 
School of Nursing
Bouvé College of Health Sciences  

Lauren Spendley
Assistant Teaching Professor 
School of Nursing
Bouvé College of Health Sciences

Abstract:  

Northeastern values experiential learning or “learning by doing.” We propose an experiential learning module to teach systems thinking skills using food forests as a tool. Systems thinking is important in environmental science because it enables students to problem solve by understanding the ecological, social, and economic dimensions of complex environmental issues. Concept mapping is proposed as a method to evaluate teaching and student learning. We hope that this poster can be applicable to other professors designing experiential learning modules. 

Presenters: 

Mariana Valencia Mestre
Assistant Teaching Professor 
Marine and Environmental Science  

College of Science

Vivian de la Paz
Student 
Marine and Environmental Science 
College of Science

Iva Chee
Student 
Marine and Environmental Science 
College of Science

Izabella Przywozny
Student 
Marine and Environmental Science 
College of Science

Abstract:

This poster presents an experiential learning course that integrates artificial intelligence, social impact, and student leadership through a project-based approach taught in partnership with a local jail. Open to undergraduate and graduate students from all majors, the course brings interdisciplinary teams into a carceral setting to design and lead workshops focused on career design and entrepreneurship. Students facilitate four structured sessions, integrating AI into each session.

By teaching familiar professional competencies in an unfamiliar context, the course deepens students’ understanding of empathy, equity, and responsibility. The session will consider transferable strategies for expanding opportunities for student-led experiential learning.

Presenters:

Michelle Zaff
Senior Cooperative Education Coordinator and Criminal Law Lecturer
Criminal Justice
College of Social Sciences and Humanities

Jen Guillemin
Senior Cooperative Education Faculty
D’Amore-McKim School of Business

Abstract:

Generative AI tools present both opportunities and risks in higher education — supporting student learning while potentially undermining academic integrity. Study Partner Builder is a Claude-based tool designed to help faculty create customized AI study partners for their courses. Through a structured, 10-minute interactive chat, the tool guides faculty through questions about course objectives, learning goals, and pedagogical philosophy, then automatically generates a ready-to-deploy AI system prompt tailored to their specific course that can be distributed amongst students to be used to create a Claude Project. The resulting project is a Study Partner designed to guide students through hints, questions, and targeted feedback, never by providing direct answers or completing work on their behalf. A key design innovation is a self-evaluation protocol within the generated prompt, which instructs the AI to assess its own responses before delivering them, ensuring academic integrity principles. This project demonstrates how prompt engineering can uphold pedagogical values at scale.

Presenters:

Sebastian Thomas
AI Instructional Assistant, CATLR
MS Artificial Intelligence, ‘26
Khoury College of Computer Science

Muhammad Salman
AI Instructional Assistant, CATLR
MS Electrical and Computer Engineering, ‘26
College of Engineering

Abstract: 

The rapid advancement of artificial intelligence (AI) has significantly transformed the field of education and software development. AI coding tools such as GitHub Copilot, and other AI-assisted programming environments are reshaping how programming is taught and learned. These tools provide real-time code suggestions, explanations, debugging support, and automated feedback, potentially enhancing the teaching and learning process. As programming education evolves to meet industry demands, integrating AI coding tools into computer science classrooms has become increasingly important. Being familiar with AI coding tools can prepare students for real-world programming environments. The research work focuses on understanding effective teaching practices and guidelines on using AI coding tools to help educators better prepare their students to meet industry demands.

Presenter:

Sarita Singh
Associate Teaching Professor
Khoury College of Computer Science