About

Sadhana Puntambekar holds the position of Sears-Bascom Professor in the Learning Sciences Area at the University of Wisconsin-Madison. She is part of the Department of Educational Psychology within the School of Education. Her role involves contributing to the research and academic community focused on learning sciences, with a specific emphasis on understanding and improving educational processes through scientific inquiry.

Research topics

• Computer Science
• Mathematics education
• Psychology
• Pedagogy
• Artificial Intelligence
• Multimedia
• Engineering management
• Medicine
• Programming language
• Engineering

Selected publications

• Generative recommendation of customized instructional activities for virtual laboratories

  Holmes Museum Of Anthropology (Wichita State University) · 2026-03-24

  other

  Poster project completed at Wichita State University, School of Computing

  Publisher

• Teacher Agency in Implementing Automated Writing Evaluation Systems in a Science Classroom: A Focus Group Study

  Lecture notes in computer science · 2025-09-02

  book-chapterSenior author
  PublisherDOI

• Instructional Goal-Aligned Question Generation for Student Evaluation in Virtual Lab Settings: How Closely Do LLMs Actually Align?

  ArXiv.org · 2025-10-07

  preprintOpen access

  Virtual Labs offer valuable opportunities for hands-on, inquiry-based science learning, yet teachers often struggle to adapt them to fit their instructional goals. Third-party materials may not align with classroom needs, and developing custom resources can be time-consuming and difficult to scale. Recent advances in Large Language Models (LLMs) offer a promising avenue for addressing these limitations. In this paper, we introduce a novel alignment framework for instructional goal-aligned question generation, enabling teachers to leverage LLMs to produce simulation-aligned, pedagogically meaningful questions through natural language interaction. The framework integrates four components: instructional goal understanding via teacher-LLM dialogue, lab understanding via knowledge unit and relationship analysis, a question taxonomy for structuring cognitive and pedagogical intent, and the TELeR taxonomy for controlling prompt detail. Early design choices were informed by a small teacher-assisted case study, while our final evaluation analyzed over 1,100 questions from 19 open-source LLMs. With goal and lab understanding grounding questions in teacher intent and simulation context, the question taxonomy elevates cognitive demand (open-ended formats and relational types raise quality by 0.29-0.39 points), and optimized TELeR prompts enhance format adherence (80% parsability, >90% adherence). Larger models yield the strongest gains: parsability +37.1%, adherence +25.7%, and average quality +0.8 Likert points.

  PublisherOA PDFDOI

• Visualizing Collaboration Using Multiple Modalities

  Computer-supported collaborative learning/˜The œComputer-Supported Collaborative Learning Conference · 2025-06-10

  articleOpen accessSenior author

  Collaboration is a multidimensional process.Our case study explores ways to visualize these multiple dimensions within a collaborating group, incorporating both verbal and nonverbal interactions while considering the individual and group, as well as temporality.We discuss the affordances and limitations of these visualizations on the insights they provide.

  PublisherOA PDFDOI

• Examining Support Moments With a Conversational AI Partner

  Computer-supported collaborative learning/˜The œComputer-Supported Collaborative Learning Conference · 2025-06-10 · 1 citations

  articleOpen accessSenior author

  AI systems can facilitate collaborative learning, but few studies examine how conversational AI partners can support collaboration.This study continues our application of the MOSAIC-AI protocol to explore support moments between a collaborative group and an educational expert serving as a Wizard-of-Oz (WoZ) agent.We present our refinement of the protocol, explore the nature of student-AI interactions, and discuss implications for building a dialog policy for informing the design of fully autonomous agents to support collaboration.

  PublisherOA PDFDOI

• Synergizing the scaffolds from teacher and digital text tool for progressive inquiry

  Interactive Learning Environments · 2025-04-17 · 1 citations

  articleSenior author
  PublisherDOI

• Teachers as Collaborators to Design Automated Feedback for Students’ Written Science Explanations (Poster 6)

  2025-01-01

  article1st authorCorresponding
  PublisherDOI

• Towards Actionable Collaborative Discourse Analysis: Bridging Advanced Computational Analysis with Practical Implementation

  Computer-supported collaborative learning/˜The œComputer-Supported Collaborative Learning Conference · 2025-06-10

  articleOpen access

  Since the inception of learning analytics, the CSCL community has been applying a range of methods to examine collaborative discourse, such as natural language processing and networked approaches.Despite significant advancements in methods, a critical gap persists in translating these data-driven insights into actionable strategies to inform pedagogical decisions.The proposed hybrid symposium seeks to bridge this gap by engaging learning scientists, CSCL researchers, and educators in discussions that integrate advanced discourse analytics with pedagogical design.Through five diverse presentations, the symposium will explore the integration of advanced discourse analysis techniques, such as networked approaches, multimodal analytics, and clustering analysis, into pedagogically meaningful designs and actionable pedagogical insights for collaborative learning environments.By fostering dialogue on the challenges and opportunities in contextualizing computational insights, this symposium aims to establish actionable approaches for enriching collaborative discourse and advancing the broader CSCL community's theoretical, methodological, and practical understanding.

  PublisherOA PDFDOI

• <scp>NLP</scp> ‐enabled automated assessment of scientific explanations: Towards eliminating linguistic discrimination

  British Journal of Educational Technology · 2025-05-24 · 3 citations

  articleSenior author

  Abstract As use of artificial intelligence (AI) has increased, concerns about AI bias and discrimination have been growing. This paper discusses an application called PyrEval in which natural language processing (NLP) was used to automate assessment and provide feedback on middle school science writing without linguistic discrimination. Linguistic discrimination in this study was operationalized as unfair assessment of scientific essays based on writing features that are not considered normative such as subject‐verb disagreement. Such unfair assessment is especially problematic when the purpose of assessment is not assessing English writing but rather assessing the content of scientific explanations. PyrEval was implemented in middle school science classrooms. Students explained their roller coaster design by stating relationships among such science concepts as potential energy, kinetic energy and law of conservation of energy. Initial and revised versions of scientific essays written by 307 eighth‐grade students were analyzed. Our manual and NLP assessment comparison analysis showed that PyrEval did not penalize student essays that contained non‐normative writing features. Repeated measures ANOVAs and GLMM analysis results revealed that essay quality significantly improved from initial to revised essays after receiving the NLP feedback, regardless of non‐normative writing features. Findings and implications are discussed. Practitioner notes What is already known about this topic Advancement in AI has created a variety of opportunities in education, including automated assessment, but AI is not bias‐free. Automated writing assessment designed to improve students' scientific explanations has been studied. While limited, some studies reported biased performance of automated writing assessment tools, but without looking into actual linguistic features about which the tools may have discriminated. What this paper adds This study conducted an actual examination of non‐normative linguistic features in essays written by middle school students to uncover how our NLP tool called PyrEval worked to assess them. PyrEval did not penalize essays containing non‐normative linguistic features. Regardless of non‐normative linguistic features, students' essay quality scores significantly improved from initial to revised essays after receiving feedback from PyrEval. Essay quality improvement was observed regardless of students' prior knowledge, school district and teacher variables. Implications for practice and/or policy This paper inspires practitioners to attend to linguistic discrimination (re)produced by AI. This paper offers possibilities of using PyrEval as a reflection tool, to which human assessors compare their assessment and discover implicit bias against non‐normative linguistic features. PyrEval is available for use on github.com/psunlpgroup/PyrEvalv2 .

  PublisherDOI

• The Role of Teacher Framing in Shaping Student Agency in Human-AI Partnered Science Classrooms

  Proceedings. · 2025-06-10

  articleOpen accessSenior author

  This study is part of a larger research project aimed at developing and implementing an NLP-enabled AI feedback tool called PyrEval to support middle school students' science explanation writing.We explored how human-AI integrated classrooms can invite students to harness AI tools while still being agentic learners.Building on theory of new materialism with posthumanist perspectives, we examined teacher framing to see how the nature of PyrEval was communicated, thereby orienting students to partner with or rely on PyrEval.We analyzed one teacher's talk in multiple classrooms as well as that of students in small groups.We found student agency was fostered through teacher framing of (a) PyrEval as a non-neutral actor and a co-investigator and (b) students' participation as an author and their understanding of the nature of PyrEval as core task and purpose.Findings and implications are discussed. Research questionsOur overall inquiry was to explore if and how human-AI integrated classrooms invited students to harness AI tools while still being agentic learners.Ultimately, we are interested in how human-AI partnered classrooms can be designed to help students see themselves as agents with voice and choices.Our research questions were:1. What meta-communicative signals does the teacher use to frame the nature of AI and students' engagement with AI, and why? 2. How does the framed nature of AI relate to students' sensemaking of AI and agency in their activities?

  PublisherOA PDFDOI

Recent grants

• Bio-Sphere: Fostering Deep Learning of Complex Biology for Building Our Next Generation's Scientists

  NSF · $2.8M · 2014–2020

• Science Inquiry Using Physical and Virtual Experiments: Systematic Investigation of Issues and Conditions for Learning

  NSF · $1.5M · 2014–2020

Frequent coauthors

• Dana Gnesdilow

  32 shared

• Indrani Dey

  25 shared

• N. Sanjay Rebello

  22 shared

• Leanne Hirshfield

  19 shared

• Nikhil Krishnaswamy

  18 shared

• James Pustejovsky

  18 shared

• Rachel Dickler

  18 shared

• Mariah Bradford

  17 shared

Education

• Ph.D., Educational Psychology

  University of Wisconsin–Madison

  2000

• M.A., Educational Psychology

  University of Wisconsin–Madison

  1996

• B.A., Psychology

  University of Mumbai

  1992

Awards & honors

• Fellow, International Society of the Learning Sciences (2018…
• Vilas Associate Award, UW-Madison (2020)
• Early CAREER Award, National Science Foundation (2000)
• Gold Medalist (1st student in class) Master’s program in Psy…
• National Merit Scholar, Osmania University (1981, 1983)