About

Bodong Chen is a learning scientist and educational technologist dedicated to making learning a meaningful part of social participation for individuals from diverse backgrounds and circumstances. His scholarly inquiry integrates knowledge media design, software engineering, and data science methods to continually improve infrastructures for learning. Guided by design-based research and participatory design approaches, he aims to generate justice-oriented pedagogical designs, technological innovations, and empirical understandings of learning in authentic settings. His research draws on multiple theoretical perspectives of learning and is interdisciplinary in nature. One focus of his scholarship is to empower students’ epistemic agency and collective responsibility by devising pedagogical designs and computer environments for K–12 classrooms. His work in this area involves designing computer tools and pedagogical interventions that engage elementary school students in developing higher-order competencies, such as identifying promising ideas and debugging collaborative discourse. He also works on customizing web technologies to connect high school science inquiry with public discourse on issues like the Green New Deal, and promoting sophisticated collaboration among college students through classroom-orchestration tools. Another key area of his research applies data science methods to rich educational data to derive actionable insights and inform educational practice. His work addresses all steps of the learning analytics cycle, utilizing computational methods such as network analysis and natural language processing to examine student dialogues and measure cultural relevance of teaching materials. He develops learning analytics applications, including student-facing dashboards, to support reflection and self-regulation, infusing participatory and value-sensitive design principles to ensure trust and rigor. Bodong Chen serves on editorial boards of several journals, is involved in professional societies, and has led and co-chaired various committees. His current funded projects include developing AI-enhanced tools for science reading assignments and innovating infrastructures for knowledge building, with previous projects focusing on justice-oriented data science education, large-scale learning data analysis, and connecting web annotations with public discourse in science classrooms.

Research topics

• Computer Science
• Psychology
• Artificial Intelligence
• Sociology
• Multimedia
• Mathematics education
• Pedagogy
• World Wide Web

Selected publications

• Advancing collaborative discourse through knowledge synthesis

  International Journal of Computer-Supported Collaborative Learning · 2026-01-19 · 2 citations

  articleOpen access

  Abstract Productive collaborative discourse requires students to continuously advance ideas, often through the creation, modification, and integration of digital artifacts in a communal space. Without these processes, ideas remain isolated, fragmented, and unable to advance shared understanding. To support such discourse processes, this study proposes a knowledge synthesis (KS) intervention to facilitate a process of creating knowledge syntheses from ideas represented in digital artifacts and then leveraging these knowledge syntheses, represented in new digital artifacts, to deepen student collaboration. To examine the enactment of this intervention in a graduate-level course, we asked: What were the key characteristics of students’ knowledge synthesis artifacts? How did student groups use the synthesis artifacts during their discourse? To what extent did the synthesis artifacts facilitate collaborative discourse? We analyzed multiple data sources—including student-created synthesis artifacts, perception data, classroom video recordings, and co-constructed group artifacts—using a combination of descriptive, content, and interaction analyses. Findings revealed diverse approaches to knowledge synthesis and showed that synthesis artifacts facilitated discourse progression, fostered a range of knowledge practices, and supported the evolution of group artifacts. By promoting knowledge synthesis and examining its role in collaborative discourse, this study contributes to computer-supported collaborative learning (CSCL) by advancing the theoretical understanding of knowledge synthesis and offering pedagogical strategies for supporting this practice in classrooms.

  PublisherOA PDFDOI

• Technology Design for Epistemic Niche Construction in the AI Era

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Technology Design for Epistemic Niche Construction in the AI Era

  2026-04-16

  article1st authorCorresponding

  Generative AI is restructuring the epistemic environments in which humans think, learn, and produce knowledge. This paper argues that epistemic niche construction---the process by which human agents build and modify the representations, tools, practices, and values that sustain their knowledge work, and are cognitively reshaped by what they build---provides a productive framework for understanding this restructuring and for guiding technology design in education. To motivate the analysis, I identify three features that make generative AI qualitatively different from prior cognitive technologies as a modifier of epistemic niches: representational opacity, generativity, and substrate asymmetry. Each produces distinctive feedback dynamics that can either enrich or erode human epistemic niches. Drawing on emerging literature across contexts, I present different enrichment and erosion mechanisms and discuss implications for designing epistemic environments for learning and knowledge work in the AI era.

  PublisherDOI

• Sense of promisingness: towards creative expertise through knowledge-building discourse in online graduate education

  Educational Technology Research and Development · 2025-03-15 · 2 citations

  articleOpen access

  Abstract This case study investigates how a designed learning environment cultivates a “sense of promisingness”—an ability to discern what may work in uncertain conditions, essential for creative expertise. The study focused on 32 in-service teachers enrolled in a Master’s program in Taiwan, who engaged in mutually-supportive development of their thesis proposals over 18 weeks using the Knowledge Forum (KF) online tool. To evaluate the development of a sense of promisingness, we analyzed online discussion content where participants expressed their understanding of “what may work, how, and why” for improving their thesis proposals. We particularly examined three types of discourse moves—sharing-, argument-, and integration-oriented—to identify conditions fostering this ability. Key findings revealed that (1) sharing-oriented discourse was necessary but not sufficient for promoting the sense of promisingness, and (2) increased effort in integration-oriented discourse moves correlated with a higher likelihood of developing this ability. The study demonstrates that the course activity, designed based on knowledge-building principles in KF, serves as an effective instructional intervention for cultivating a sense of promisingness among graduate students. This research addresses a gap in the literature regarding methods to develop such knowledge-creating sense and the role of different discourse moves in sustaining knowledge advancement in online learning environments. The paper concludes with design implications for similar educational contexts.

  PublisherOA PDFDOI

• Educators’ AI Journey: Developing AI Competencies in a Professional Development Program

  Communications in computer and information science · 2025-12-01

  book-chapterSenior author
  PublisherDOI

• Pedagogical Biases in AI-Powered Educational Tools: The Case of Lesson Plan Generators

  2025-04-02 · 7 citations

  preprintOpen access1st authorCorresponding

  This paper examines pedagogical biases in AI-powered educational tools, focusing specifically on lesson plan generators. We investigate how these tools may implicitly embed outdated educational approaches that limit student agency and classroom dialogue. Through analysis of 90 lesson plans from commercial lesson plan generators, we found that AI-generated content predominantly promotes teacher-centered classrooms with limited opportunities for student choice, goal-setting, and meaningful dialogue. To mitigate this issue, we further experimented with intentional prompt engineering, which showed promise in significantly enhancing these dimensions in AI-generated lesson plans. We offer practical strategies for educators and developers to mitigate harmful pedagogical biases while promoting contemporary educational values. This work contributes to the critical conversation about how AI tools should be designed and used to support, rather than undermine, the future of education that values student agency and productive classroom dialogue.

  PublisherDOI

• Collaborating with Generative AI for Learning?

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

  articleOpen access

  Generative Artificial Intelligence (GenAI) tools, driven by large language models (LLMs), are increasingly explored for their potential in educational contexts.However, significant concerns remain regarding their efficacy, cognitive impacts, and ethical implications.The Computer Supported Collaborative Learning (CSCL) community faces critical questions regarding the broader implications of this technology for the field.While GenAI offers advanced opportunities for collaborative learning through conversational interactions and other functions, research in this area is still in its early stages.This symposium presents five contributions that explore the opportunities, challenges, and initial findings of integrating GenAI into educational settings.The goal is to provide evidence-based recommendations for practice and identify key research directions and challenges for the future of CSCL in the context of GenAI.

  PublisherOA PDFDOI

• The Evolution of Shared Artifacts in CSCL: A Knowledge Synthesis Intervention for Productive Collaborative Discourse

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

  articleOpen accessSenior author

  In CSCL, productive collaborative discourse requires continual advancement of student ideas, often through the creation and modification of digital artifacts.To support this process, this study proposes a knowledge synthesis intervention, supported by technological and pedagogical designs, to facilitate the continual development of digital artifacts in a graduate class.To examine the enactment of this intervention, we asked: How did the knowledge synthesis artifacts facilitate the progression of collaborative discourse?How did they facilitate the development of shared group artifacts?Findings revealed diverse ways in which synthesis artifacts facilitated discourse navigation, fostered various knowledge practices, and facilitated the evolution of group artifacts.This study contributes to CSCL and online learning research by facilitating knowledge synthesis and examining its role in deepening collaborative discourse, generating technological and pedagogical designs applicable in broader contexts. Facilitating productive discourse through knowledge synthesisResearch from various disciplines has examined processes and concepts related to knowledge synthesis, sometimes under different terms or in implicit manners.For example, information science scholars have studied how researchers synthesize literature both individually and cooperatively for scientific inquiry (Morabito & Chan, 2021;Qian et al., 2020).In the context of education, Linn (2006) demonstrated the benefits of knowledge synthesis, referred to as knowledge integration, in supporting learners to construct a more comprehensive and nuanced understanding of scientific subjects.Moreover, the Knowledge Building model emphasizes the notion of "rise above" to build on previous ideas, leading to the development of novel knowledge (Scardamalia & Bereiter, 2014).While research from various fields has highlighted various facets of knowledge synthesis, these insights

  PublisherOA PDFDOI

• The Evolution of Shared Artifacts in CSCL: A Knowledge Synthesis Intervention for Productive Collaborative Discourse

  2025-06-20

  preprintOpen accessSenior author

  In CSCL, productive collaborative discourse requires continual advancement of student ideas, often through the creation and modification of digital artifacts. To support this process, this study proposes a knowledge synthesis intervention, supported by technological and pedagogical designs, to facilitate the continual development of digital artifacts in a graduate class. To examine the enactment of this intervention, we asked: How did the knowledge synthesis artifacts facilitate the progression of collaborative discourse? How did they facilitate the development of shared group artifacts? Findings revealed diverse ways in which synthesis artifacts facilitated discourse navigation, fostered various knowledge practices, and facilitated the evolution of group artifacts. This study contributes to CSCL and online learning research by facilitating knowledge synthesis and examining its role in deepening collaborative discourse, generating technological and pedagogical designs applicable in broader contexts.

  PublisherDOI

• Supporting Justice-Oriented Data Science in a Secondary Science Classroom: Pathways and Tensions

  Proceedings. · 2025-06-10

  articleOpen access

  This study explores how a secondary science teacher enacted a justice-oriented data science module in a seventh-grade life science classroom.Co-designed by the teacher and a group of researchers, the module engaged students in investigating quality of life using realworld datasets about their own neighborhoods.Classroom observations and debrief interviews revealed that the teacher effectively connected data science to life science and students' lived experiences, facilitating discussions that linked data practices with justice issues.However, challenges emerged in addressing justice topics of relevance to students.These findings underscore the need for enhanced support and professional development to help teachers navigate the complexities of facilitating justice-oriented data science in diverse K-12 contexts. Methods Context and participantThis study is situated in a design-based research project that aims to develop integrated, justice-oriented curricula, a web-based learning platform named DataX, and pedagogical practices for teaching secondary students about data science.Following a co-design approach, the project team co-developed several curricular units and iteratively refined the platform with three secondary science and social studies teachers from a large urban school district in the Midwestern United States.In this co-design partnership, the teachers first participated in five half-

  PublisherOA PDFDOI

Recent grants

• CRII: Cyberlearning: Connecting Web Annotations and Progressive Online Discourse in Science Classrooms

  NSF · $169k · 2017–2020

Frequent coauthors

• Alyssa Friend Wise

  29 shared

• Simon Knight

  University College London

  21 shared

• Marlene Scardamalia

  Aeres University of Applied Sciences

  18 shared

• Monica Resendes

  University of Toronto

  15 shared

• Xinran Zhu

  University of Pennsylvania

  14 shared

• Hong Shui

  13 shared

• Huang‐Yao Hong

  12 shared

• Carl Bereiter

  12 shared

Education

• PhD, Department of Curriculum, Teaching and Learning

  University of Toronto

  2014

Awards & honors

• Penn Global awards grants to four Penn GSE faculty projects…
• Penn Global awards $1.7 million in Research and Engagement A…