About

Alejandra J. Magana, Ph.D., is the W.C. Furnas Professor in Enterprise Excellence at Purdue University, with appointments in the Polytechnic Institute, Computer and Information Technology, and Engineering Education. Her research focuses on understanding and enhancing modeling and simulation practices in undergraduate engineering education, with particular interest in computational model-based reasoning, embodied-based learning, and the development of self-regulated learning skills in computing. She investigates how sociological factors influence computing learning within technology-enabled environments and leverages learning analytics and AI to support student learning in computing-intensive domains. Dr. Magana has contributed significantly to the field through her leadership in various funded projects, including those aimed at improving AI education through embodied visualization, fostering culturally relevant programming experiences, and integrating computation into STEM disciplines. She has been recognized with numerous honors, such as being conferred the Fellow Member grade of the American Society for Engineering Education (ASEE) in 2024, inducted into the Purdue University Teaching Academy, and receiving awards for her research contributions in modeling, simulation, and STEM education. Her work emphasizes creating innovative, inclusive, and effective educational strategies that bridge computational and engineering disciplines, with a strong focus on research-based design and dissemination to improve engineering and computing education practices.

Research topics

• Computer Science
• Psychology
• Political Science
• Artificial Intelligence
• Engineering
• Sociology
• Mathematics education
• Social Science
• Virology
• Multimedia
• Medicine
• Mathematics
• Human–computer interaction
• Software engineering
• Management science

Selected publications

• Game-Based and Gamified Robotics Education: A Comparative Systematic Review and Design Guidelines

  OSF Preprints (OSF Preprints) · 2026-01-25

  otherSenior author
  Publisher

• Artificial Intelligence and Responsible Adoption in Engineering Education: Evidence, Concerns, and a Constructive Path Forward

  Computer Applications in Engineering Education · 2026-04-03

  articleSenior author

  The rapid integration of generative artificial intelligence (AI) into educational practice has generated both enthusiasm and apprehension. For Computer Applications in Engineering Education (CAE), a journal founded on the premise that computational technologies can enhance learning effectiveness, the present moment represents not a disruption of mission, but an inflection point. Among the most frequently expressed concerns is academic integrity, and consequently the potential erosion of critical thinking skills. Has generative AI fundamentally increased cheating, or has it primarily transformed the mechanisms through which academic misconduct may occur? A balanced examination of available evidence suggests a more nuanced picture than public discourse often conveys. Recent survey data confirm that generative AI use among students is widespread. The Higher Education Policy Institute [1] reports that over 90% of surveyed UK students use generative AI tools for academic purposes. Similarly, the College Board [2] reports that more than 80% of US high school students use generative AI for school-related work. Even adult learners have reported using AI for academic work [3]. AI use is no longer peripheral; it is mainstream. Large-scale submission analytics further demonstrate measurable AI integration into student work. Turnitin [4] reports that approximately 17% of global submissions exhibit substantial AI-writing indicators. Yet adoption alone does not equate to misconduct. Emerging empirical research suggests that academic dishonesty rates may not have dramatically increased following the release of large language models. A recent study in Computers & Education found that self-reported cheating behaviors among secondary students remained statistically comparable pre- and post-ChatGPT introduction, suggesting transformation rather than explosion of misconduct patterns (e.g., comparative analyses reported in 2024). Similarly, scholars writing in the Journal of Engineering Education argue that generative AI challenges assessment design more than it fundamentally alters student ethics [5, 6]. Educator concern nevertheless remains high. The 2025 AI Index Report from Stanford's Institute for Human-Centered AI identified academic integrity and misuse as primary concerns among teachers and administrators [7]. The central tension is therefore not only uncertainty about AI use, but also uncertainty about assessment resilience. On the other hand, recent studies indicate that students in higher education use AI tools, but lack structured support and formal training skills [8]. Students want clearer institutional support, guidance, and preparation for responsible AI use and future careers. [9]. In contrast, other studies have reported on students' feelings of guilt, shame, and fear of using generative AI for academic work [10]. Thus, it is imperative that educators take action and deliver concrete guidance to students. AI-detection systems have been rapidly deployed. However, vendors and standards organizations caution against treating automated outputs as definitive evidence. The National Institute of Standards and Technology [11] emphasized broader reliability and risk-management challenges inherent in evolving AI systems. False positives, paraphrasing, hybrid human–AI writing, and model drift complicate enforcement decisions. Peer-reviewed discussions in engineering education similarly caution that reliance on detection technologies may produce procedural fairness concerns and may inadvertently penalize multilingual or stylistically distinctive writers [6]. Detection tools may serve as preliminary screening mechanisms, but they cannot replace sound pedagogical design. International policy guidance increasingly advocates for governance frameworks grounded in transparency and AI literacy rather than prohibition. UNESCO [12] recommends clear institutional policies, disclosure practices, and educator capacity building. Process-based evaluation (drafts, checkpoints, design notebooks). Reflective memos documenting reasoning and iteration. Oral defense components or short explanatory interviews. Contextualized assignments tied to local data or laboratory work. Explicit AI-use disclosure expectations. Integration of AI literacy as a learning outcome. Such strategies shift evaluation from determining whether AI was used to determining whether understanding has been demonstrated. Engineering education occupies a uniquely advantageous position in this transition. For more than three decades, CAE has promoted simulation-driven learning, computational modeling, and digital laboratories. Generative AI may be understood as a continuation of this computational trajectory. The core pedagogical question is not whether students consult AI systems, but whether they use them without forgoing learning, and whether assessments effectively measure modeling judgment, parameter selection, validation reasoning, and design trade-offs. These competencies resist superficial outsourcing. As Magana et al. [13] argue in the Journal of Engineering Education, generative AI can be integrated productively into engineering research and learning workflows when guided by structured pedagogical frameworks. Engineering education, therefore, may serve as a proving ground for responsible AI integration rather than a casualty of its misuse. Students can also be equipped with strategies that provide them with learning agency when using generative AI, so that they can develop self-regulated learning in this context. That is, students develop agency when they feel confident when using generative AI (dispositional agency), when they have access to generative AI tools, along with institutional support (positional), and when they have motivation, goals, and choice in their uses of generative AI (motivational) [14]. Once students develop such forms of learning agency, they can develop the capability to self-regulate their learning when using such tools, so that they plan, monitor, and evaluate the consequences of using them for academic work, without sacrificing their learning [15]. Rather than framing generative AI solely as a threat to academic integrity, CAE advocates for principled innovation grounded in evidence, transparency, and pedagogical rigor. The responsibility before engineering educators is not to retreat from technological change, but to shape it. Develop transparent AI-use policies aligned explicitly with course learning objectives and professional ethics. Redesign assessments to emphasize reasoning, modeling judgment, iteration, validation, and design trade-offs. Integrate AI literacy and AI learning agency as a technical and professional competency within engineering curricula. Conduct rigorous empirical studies evaluating AI-integrated assessment frameworks. Disseminate validated practices through peer-reviewed scholarship that distinguishes evidence from anecdote. Generative AI is unlikely to recede from educational environments. The central question is therefore not whether AI will be present, but whether engineering education will lead in defining its responsible, pedagogical, and effective use. Since its founding in 1992, CAE has consistently advanced the thoughtful integration of computational tools, simulation environments, multimedia learning modules, virtual laboratories, and data-driven instructional strategies. Each technological wave—from desktop computing to web-based learning, from CAD systems to high-fidelity modeling—initially raised concerns about rigor, dependency, and integrity. In each case, engineering education responded not by lowering standards, but by refining them. Generative AI represents the next phase in this computational evolution. Upholding academic integrity through pedagogical strength rather than technological surveillance alone. Promoting research that differentiates responsible and pedagogical AI integration from misuse. Identifying and validating strategies for generative AI uses that empower students to succeed in modern engineering workplaces, without compromising their learning. Encouraging assessment models that measure deep understanding rather than surface production. Providing a scholarly forum where innovation is examined with methodological rigor and professional dignity. Leading international dialog on AI in engineering education grounded in evidence, not rhetoric. In doing so, CAE does not position itself as reacting to the AI wave, but as continuing a long-standing mission: advancing digital technologies to enhance learning effectiveness and elevate engineering education globally. The integrity of engineering education will not be preserved by resisting AI, but by embedding it within principled, research-based pedagogy. The opportunity before us is not merely to manage risk, but to define standards. CAE stands committed to leading this effort—thoughtfully, rigorously, and with the dignity befitting a journal that has served the field for over three decades. The challenge is real. The opportunity is greater. The responsibility is ours. Generative AI tools were used during manuscript preparation to assist in identifying and synthesizing publicly available literature related to artificial intelligence in engineering education. The author takes full responsibility for the interpretation and conclusions presented. References are provided for all cited literature. This work was supported in part by the U.S. National Science Foundation under award numbers 2434429 and 2315683. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the National Science Foundation. The authors declare no conflicts of interest. The data that support the findings of this study are available from the corresponding author upon reasonable request.

  PublisherDOI

• ExPeerience: Towards AI-Assisted Learnersourcing to Bridge Conceptual Understanding and Problem Solving in Database Programming Education

  2026-03-03 · 2 citations

  articleOpen access

  Learnersourcing, an educational approach that positions students as active contributors rather than passive consumers, offers a scalable approach to co-creating instructional resources while engaging students in authentic problem-solving. However, it faces a fundamental tension: effective “learning” requires scaffolding that minimizes extraneous cognitive load and focuses attention on reasoning, while effective “sourcing” requires structure, completeness, and standardization to ensure student-generated content can be reused. These competing goals create a tradeoff: students either learn but produce content that is difficult to reuse, or generate usable resources but receive limited learning benefit. We propose a new AI-assisted learnersourcing paradigm to address this tension. By assigning collaborative roles to both learners and AI, the approach enables students to focus on cognitively meaningful sub-tasks that foster “learning”, while large language models (LLMs) handle mechanical and procedural sub-tasks for “sourcing”. Guided by user-centered design principles, we implement this workflow in ExPeerience, a system that scaffolds students in co-creating contextualized worked-out examples for database programming. Within ExPeerience, the AI serves as a collaborator for ideation, a co-creator of artifacts, and an evaluator of students’ inputs. Our evaluation with 24 participants showed that structuring AI into distinct collaborative roles improves learning engagement while producing high-quality student-generated content. Compared to a baseline using the Gemini chatbot, ExPeerience users created SQL problems in more diverse and personally meaningful contexts. They actively evaluated, edited, and refined AI-generated components, and most authored their own SQL solutions, whereas baseline participants largely accepted AI outputs without modification and did not attempt to solve the problem. Overall, ExPeerience produced more contextualized, varied, and thoughtfully constructed worked-out examples. These findings demonstrate the potential of AI-assisted learnersourcing as a paradigm to balance learning and sourcing goals. We also draw design implications for future AI-assisted learnersourcing systems that aim to produce reusable, high-quality learner-generated content while promoting educational value.

  PublisherDOI

• Feedback Systems on Computer Programming Courses: A Systematic Literature Review

  IEEE Access · 2026-01-01

  articleOpen accessSenior author

  Feedback is critical throughout the education process. Relevant and timely feedback is difficult to provide in classes with a large number of students, such as computer programming courses. This problem is common in scenarios such as first-year undergraduate education or massive open online courses, where the students have little chance to receive detailed feedback, usually reducing it to a right or wrong answer or to a general discussion about the most common mistakes in the course. Automatically generated feedback can help instructors and learners by personalizing content via just-in-time interventions. Following PRISMA guidelines, this paper used a systematic approach to review the literature about automatically generated feedback for computer programming education. Findings from the study provide a diverse set of approaches for providing feedback, from static ones based on unit tests to dynamic systems using machine learning and Large Language Models. Findings also indicate how different approaches delivered such feedback, including how such systems’ effectiveness was tested (if available), the type of feedback given, and the computational method they implemented. The most common approach was static analysis, with bugs of misconceptions being the most common type of feedback (38%). While not all the reviewed studies tested for educational outcomes, those that did mostly used surveys (>90%) and grades. Such surveys primarily measured participants’ perceptions and motivation. AI-based feedback decision methods are increasingly used, taking advantage of Large Language Models, which enable instructors to provide dynamic feedback. These latest developments will be in permanent improvement as they are personalized, more comprehensive, and provide several types of feedback.

  PublisherDOI

• ExPeerience: Support Student-AI Co-Creation in a Learnersourcing Workflow

  2026-03-09

  articleOpen access

  This demo presents ExPeerience, an AI-assisted learnersourcing system that supports students in co-creating contextualized worked-out examples for database programming. Learnersourcing is a pedagogical approach in which students act as contributors by creating learning materials as part of the learning process. The system addresses two challenges in learnersourcing: (1) the high cognitive burden students face during content creation, and (2) the difficulty of balancing student learning with the production of reusable, high-quality sourced learning materials. ExPeerience structures student–AI collaboration by assigning the AI distinct roles across ideation, content generation, and solution evaluation, while keeping students responsible for decision-making and problem-solving.

  PublisherDOI

• Game-Based and Gamified Robotics Education: A Comparative Systematic Review and Design Guidelines

  2026-04-13 · 1 citations

  articleOpen access

  Robotics education fosters computational thinking, creativity, and problem-solving, but remains challenging due to technical complexity. Game-based learning (GBL) and gamification offer engagement benefits, yet their comparative impact remains unclear. We present the first PRISMA-aligned systematic review and comparative synthesis of GBL and gamification in robotics education, analyzing 95 studies from 12,485 records across four databases (2014–2025). We coded each study’s approach, learning context, skill level, modality, pedagogy, and outcomes (κ =.918). Three patterns emerged: (1) approach–context–pedagogy coupling (GBL more prevalent in informal settings, while gamification dominated formal classrooms [p <.001] and favored project-based learning [p =.009]); (2) emphasis on introductory programming and modular kits, with limited adoption of advanced software (~17%), advanced hardware (~5%), or immersive technologies (~22%); and (3) short study horizons, relying on self-report. We propose eight research directions and a design space outlining best practices and pitfalls, offering actionable guidance for robotics education.

  PublisherDOI

• Spatial Exploration Behavior in XR Learning: Toward Passive Assessment of Embodied Engagement

  2026-03-21

  article
  PublisherDOI

• Model-based reasoning in STEM education: a systematic literature review

  International Journal of STEM Education · 2026-05-24

  articleOpen accessSenior author

  This systematic review examines model-based reasoning (MBR) in STEM education, focusing on how it is defined, how it works in practice, and how it is measured and taught across classrooms and laboratories. Guided by PRISMA 2020, this review synthesized 146 peer-reviewed studies published between 1980 and 2025 to address four research questions. We report a qualitative synthesis and descriptive frequencies from the findings. First, the literature converges on MBR as an iterative, distributed, and representation-mediated practice that links mental models with external inscriptions, including diagrams, equations, prototypes, code, and simulations, while integrating abductive, inductive, deductive, causal-mechanistic, and computational forms of reasoning. Second, the reviewed studies suggest recurring stage-based patterns in modeling and simulation activities: abductive and analogical reasoning are especially visible during early problem analysis and formulation; deductive, quantitative, and algorithmic reasoning support model construction and execution; diagnostic, inductive, and probabilistic reasoning support verification, validation, and debugging activities. Third, the field broadly agrees on the centrality of iteration, external representations, and collaboration in model-based reasoning activities, while debates persist over primary theoretical emphasis, particularly whether model-based reasoning is best grounded in mental models or distributed cognition; whether reasoning modes should be treated as analytically separable or as hybrid in use; and the extent to which domain-specific standards should guide model evaluation and acceptance decisions. Fourth, the ways MBR is characterized and measured shape what can be claimed about it: micro-level approaches, such as think-aloud protocols and time-stamped coding, capture moment-to-moment strategy use; meso-level approaches, such as computational notebooks, simulation logs, and rubric-based assessments, reveal workflow and representational competence; and macro-level approaches, such as model-evidence link diagrams and portfolios, capture longer-term development over weeks or semesters. These findings position MBR as a useful integrative lens for scientific sensemaking that is applicable across STEM disciplines, while also showing that its meaning and assessment remain shaped by disciplinary, instructional, and methodological context.

  PublisherDOI

• A systematic literature review on hidden curricula in computing and engineering education

  European Journal of Engineering Education · 2026-03-25

  articleSenior authorCorresponding
  PublisherDOI

• Game-Based and Gamified Robotics Education: A Comparative Systematic Review and Design Guidelines

  Open MIND · 2026-01-29

  preprint

  Robotics education fosters computational thinking, creativity, and problem-solving, but remains challenging due to technical complexity. Game-based learning (GBL) and gamification offer engagement benefits, yet their comparative impact remains unclear. We present the first PRISMA-aligned systematic review and comparative synthesis of GBL and gamification in robotics education, analyzing 95 studies from 12,485 records across four databases (2014-2025). We coded each study's approach, learning context, skill level, modality, pedagogy, and outcomes (k = .918). Three patterns emerged: (1) approach-context-pedagogy coupling (GBL more prevalent in informal settings, while gamification dominated formal classrooms [p &lt; .001] and favored project-based learning [p = .009]); (2) emphasis on introductory programming and modular kits, with limited adoption of advanced software (~17%), advanced hardware (~5%), or immersive technologies (~22%); and (3) short study horizons, relying on self-report. We propose eight research directions and a design space outlining best practices and pitfalls, offering actionable guidance for robotics education.

  DOI

Recent grants

• Productive Online Teamwork Engagement Through Intelligent Mediation

  NSF · $850k · 2021–2025

• CAREER: Authentic Modeling and Simulation Practices for Enhancing Model-Based Reasoning in Engineering Education

  NSF · $500k · 2015–2021

• Haptic-Based Learning Experiences as Cognitive Mediators for Conceptual Understanding and Representational Fluency in Engineering Education

  NSF · $325k · 2016–2020

• An AI-Augmented Phenomenographic Approach to Conceptualizing Undergraduate Students Experiences of Intercultural Team Cognition in STEM

  NSF · $350k · 2022–2026

• Collaborative Research: SmartCAD: Guiding Engineering Design with Science Simulations

  NSF · $405k · 2015–2020

Frequent coauthors

• Camilo Vieira

  Universidad del Norte

  69 shared

• Joseph A. Lyon

  Purdue University System

  65 shared

• Elsje Pienaar

  Software (Spain)

  59 shared

• Joreen Arigye

  Purdue University West Lafayette

  44 shared

• Aidsa Santiago-Román

  University of Puerto Rico-Mayaguez

  37 shared

• Eric T. Matson

  36 shared

• Nayda Santiago

  University of Puerto Rico System

  36 shared

• Cesar Aceros

  Cornell University

  36 shared

Education

• Doctor of Philosophy in Engineering Education, School of Engineering Education

  Purdue University

  2009

• Master of Science in Education, Curriculum and Instruction

  Purdue University

  2007

• Master of Science, Computer Science

  Instituto Tecnologico y de Estudios Superiores de Monterrey

  2003

• Information Systems Engineering, Computer Science

  Instituto Tecnologico y de Estudios Superiores de Monterrey

  2000

Awards & honors

• 2024 - Conferred the grade of Fellow Member for the American…
• 2024 - Fulbright Specialists Czech Technical University
• 2023 - Seeds for Success Award
• 2022 - Inducted into the Purdue University Teaching Academy
• 2021 - Purdue Polytechnic Charles B. Murphy Outstanding Unde…