About

Lisa Porter is a Professor of Theatre and Dance at the University of California, San Diego, where she teaches stage management, collaboration, and topics related to integrated performance management. Her career has included international projects on six continents, working with notable artists such as Laurie Anderson, Mikhail Baryshnikov, Anne Bogart, Hal Hartley, Yo-Yo Ma, Silkroad Ensemble, White Oak Dance Project, and Robert Wilson. She has collaborated extensively on intercultural productions with Singaporean director Ong Keng Sen and TheatreWorks Singapore, and her New York and Regional credits include productions with Christopher Ashley, Doug Hughes, Charles Busch, Jonathan Demme, Richard Foreman, Tina Landau, Kenny Leon, Suzan-Lori Parks, Darko Tresnjak, and Mark Wing-Davey. Since 1996, she has also produced and stage managed non-profit and corporate events. In 2020, she co-published the book 'Stage Management Theory as a Guide to Practice: Cultivating a Creative Approach' and an essay on anti-racist stage management education. Her work has been featured in prominent publications such as the New York Times and American Theatre. As a 2020-2022 Changemaker Faculty Fellow, she co-created a project exploring the history of disability in the United States and its representation in the performing arts. Her academic research investigates the principles of inspired collaboration, focusing on the intersection of the collaborative environment with psychology, group dynamics, and neuroscience, especially within interdisciplinary and cross-cultural contexts. She has a strong interest in the relationship between disability and performance and has served as an advisor to theaters developing sensory-friendly performances. Porter holds a theatre and management degree from Earlham College and an MFA in Stage Management from Yale School of Drama.

Research topics

• Sociology
• Computer Science
• Psychology
• Social psychology
• Pedagogy
• Mathematics education
• Social Science
• Political Science
• Mathematics
• Medicine
• Demography
• Engineering

Selected publications

• Exploring Student-AI Interactions in Vibe Coding

  2026-02-09 · 1 citations

  articleOpen accessSenior author

  Background and Context. Chat-based and inline-coding-based GenAI has already had substantial impact on the CS Education community. The recent introduction of “vibe coding” may further transform how students program, as it introduces a new way for students to create software projects with minimal oversight.

  PublisherDOI

• Drawing Your Programs: Exploring the Applications of Visual-Prompting with GenAI for Teaching and Assessment

  ArXiv.org · 2026-02-11

  articleOpen access

  When designing a program, both novice programmers and seasoned developers alike often sketch out -- or, perhaps more famously, whiteboard -- their ideas. Yet despite the introduction of natively multimodal Generative AI models, work on Human-GenAI collaborative coding has remained overwhelmingly focused on textual prompts -- largely ignoring the visual and spatial representations that programmers naturally use to reason about and communicate their designs. In this proposal and position paper, we argue and provide tentative evidence that this text-centric focus overlooks other forms of prompting GenAI models, such as problem decomposition diagrams functioning as prompts for code generation in their own right enabling new types of programming activities and assessments. To support this position, we present findings from a large introductory Python programming course, where students constructed decomposition diagrams that were used to prompt GPT-4.1 for code generation. We demonstrate that current models are very successful in their ability to generate code from student-constructed diagrams. We conclude by exploring the implications of embracing multimodal prompting for computing education, particularly in the context of assessment.

  PublisherOA PDF

• Prompting through Decomposition: Evaluating the Efficacy of Problem Decomposition Diagrams for Code Generation

  2026-02-13

  articleOpen access

  When engaged in the initial design of a program, novice programmers and seasoned developers alike often sketch out---or, perhaps more famously, whiteboard---their ideas. However, with the introduction of natively multimodal Generative AI models, such diagrams may now function as a means of code generation in their own right. In this work, we perform an initial evaluation to understand how student-created decomposition diagrams can serve as prompts for code generation, with implications for teaching and assessing problem decomposition skills.

  PublisherDOI

• Exploring Student Behaviors and Motivations when using AI Teaching Assistants with Optional Guardrails

  2026-02-09

  articleOpen access

  AI-powered chatbots and digital teaching assistants (AI TAs) are gaining popularity in programming education, offering students timely and personalized feedback. Despite their potential benefits, concerns about student over-reliance and academic misconduct have prompted the introduction of “guardrails” into AI TAs—features that provide scaffolded support rather than direct solutions. However, overly restrictive guardrails may lead students to bypass these tools and use unconstrained AI models, where interactions are not observable, thus limiting our understanding of students’ help-seeking behaviors. To investigate this, we deployed a novel AI TA tool with optional guardrails in one lab of a large introductory programming course. As students completed three code writing and debugging tasks, they had the option to receive guardrailed help or use a “See Solution” feature which disabled the guardrails and generated a verbatim response from the underlying model. We investigate students’ motivations and use of this feature and examine the association between use and their course performance. We found that 50% of the 885 students used the “See Solution” feature for at least one problem and 14% used it for all three problems. Additionally, low-performing students were more likely to use this feature and use it close to the deadline as they started assignments later. The predominant factors that motivated students to disable the guardrails were assistance in solving problems, time pressure, and lack of self-regulation. Our work provides insights into students’ solution-seeking motivations and behaviors, which has implications for the design of AI TAs that balance pedagogical goals with student preferences.

  PublisherDOI

• Evaluating CS1-LLM: Integrating LLMs and Examining Student Outcomes in an Introductory Computer Science Course

  2026-02-09

  articleOpen access

  Large language models (LLMs) have broad implications for education in general, impacting the foundations of what we teach and how we assess. This is especially true in computing, where LLMs tuned for coding have demonstrated shockingly good performance on the types of assignments historically used in introductory CS (CS1) courses. As a result, CS1 courses will need to change in terms of the skills that are taught and how they are assessed. Computing education researchers have begun to study student use of LLMs, but there remains much to be understood about the ways that these tools affect student outcomes. In this paper, we present the design and evaluation of a new CS1 course at a large research-intensive university that integrates the use of LLMs for student learning. We describe the design principles used to create our course, our new course objectives, and evaluation of student outcomes and perceptions throughout the course as measured by assessment scores and surveys. Our findings suggest that 1) student exam performance outcomes, including differences among demographic groups, are largely similar to historical outcomes for courses without integration of LLM tools, 2) large, open-ended projects may be particularly valuable in an LLM context, and 3) students predominantly found the LLM tools helpful, although some had concerns regarding over-reliance on the tools.

  PublisherDOI

• Enabling Open Educational Resource Adoption through Integrated Sharing in PrairieLearn

  2026-02-13

  articleOpen access

  This paper introduces the PrairieLearn Question Sharing System (PQSS), which enables instructors to share question generators with other instructors, either as open educational resources or privately. PQSS is integrated into PrairieLearn, an open-source, problem-driven online learning platform. PQSS addresses a critical need for more open-source assessments by making it easier for instructors to share assessments and for instructors to use those assessments. Instructors often do not share questions due to the time it takes to publish them and the lack of recognition for their work. Because it is directly integrated into PrairieLearn, PQSS reduces the aforementioned friction of sharing and using shared questions, and we can report usage statistics to help question authors receive recognition for their work. In this paper, we share design and implementation details of the system, as well as experiences using it to share course content across courses and between universities.

  PublisherDOI

• Teaching Presence: Discussion Board Participation in a Prison-Based Computer Architecture Course

  2026-02-13

  articleOpen access

  Incarcerated students face profound barriers to learning computer science: restricted internet access, limited technology, and highly constrained peer interaction. In this environment, the learning management system (LMS) becomes not only a tool but a central site for teaching, collaboration, and meaning-making. This poster presents a quantitative LMS trace analysis of a Winter 2025 computer architecture course taught in a California prison, examining how teaching presence (professor/TA facilitation and response timing) related to discussion board activity, LMS page views, and grade outcomes through the lens of the Community of Inquiry (CoI) framework. Using Canvas API exports of discussion board content, page views, and grades, we analyzed participation counts by role, thread depth, staff response times, and correlations between engagement and final percentage. We extend prior work by providing quantitative and temporal visualizations of participation, summarizing posting patterns across the 10-week course. We also report preliminary indicators of message content characteristics and clarify our protocol for distinguishing staff and student contributions. While these analyses represent a single cohort, they offer early evidence that timely teaching presence may support engagement in constrained environments and suggest directions for broader application in online, hybrid, and other resource-limited CS courses.

  PublisherDOI

• Teaching Computing in Prison

  2026-02-13

  articleOpen accessSenior author

  This is a place to foster a growing community of computing educators around teaching in prison – for those who are interested in possibly doing this in the future, and those with plans or experience doing so. Higher education in prison (HEP) has expanded rapidly in the U.S. over the past several years after a policy change that re-instated pell grant eligibility to incarcerated adults. This follows a global shift toward more rehabilitative, as opposed to punitive, strategies toward criminal justice as more countries recognize the wide-ranging benefits to all members of society. However, computing education, as well as basic digital literacy, remain a challenge as various factors like technology infrastructure, perceived threats to security, and instructor willingness remain challenges to offering CS courses in prison education programs. Despite these barriers, there are several models of CS education happening in prison today including in-person and virtual instruction, for-credit college courses and informal workshops. In this session, we will talk about different ways of getting started with teaching in prison, as well as practical strategies for navigating challenges from our own personal experience of teaching CS in prison settings.

  PublisherDOI

• Further Study of Leveraging LLM Tutoring Systems for Non-Native English Speakers in Introductory Computer Science

  2026-02-09

  articleOpen accessSenior author

  A recent study investigating the effectiveness of a large language model (LLM) tutor in supporting non-native English-speaking students (NNES) in introductory computer science courses showed promising results. It demonstrated that LLMs can be leveraged to better support NNES by facilitating learning in their native languages.

  PublisherDOI

• Generative <scp>AI</scp> Solves Introductory Computer Science Tasks. Now What?

  2026-01-09

  other1st authorCorresponding

  This chapter presents a case as to why introductory computer science (CS) courses need to change in light of generative artificial intelligence (GenAI). It outlines programming skills that will be foundational for new students to master when working with GenAI. There are a number of foundational skills that are crucial when programming with a GenAI assistant. These include problem decomposition, prompting and working with GenAI, and testing and debugging. The chapter describes each of these necessary skills and why they have gained importance when working with GenAI. There are many ways to incorporate these skills into a CS1 course. For concreteness, the chapter also presents how students chose to integrate them into their CS1-large language models course. Students need to practice generating decompositions whose functions have the right level of granularity: functions that are too big are error-prone, and functions that are too small clutter up the code.

  PublisherDOI

Recent grants

• Collaborative Research: Peer Instruction in Computer Science

  NSF · $58k · 2012–2015

• Collaborative Research: Infrastructure and Development of a Computer Science Concept Inventory for CS2

  NSF · $126k · 2015–2020

• Collaborative Research: A New Computer Science Faculty Teaching Workshop

  NSF · $32k · 2014–2019

• Collaborative Research: Understanding the Impact of Low Prerequisite Proficiency on Student Success in Computer Science

  NSF · $266k · 2021–2025

• Identifying and Aiding At-Risk Students in Computing

  NSF · $315k · 2017–2021

Frequent coauthors

• Daniel Zingaro

  38 shared

• William G. Griswold

  University of California, San Diego

  27 shared

• Beth Simon

  UPMC Hillman Cancer Center

  19 shared

• Cynthia Lee

  Palo Alto University

  18 shared

• Liz Jones

  UK Health Forum

  16 shared

• Soohyun Nam Liao

  University of California, San Diego

  15 shared

• Cynthia Taylor

  14 shared

• Sophia Krause-Levy

  University of San Diego

  13 shared

Education

• Other, Theatre and Dance

  University of California, San Diego

• B.A., Theatre and Dance

  University of California, San Diego

Awards & honors

• 2020-2022 Changemaker Faculty Fellow