Collaborative Research: STEM Learning Embedded in a Machine-in-the-Loop Collaborative Story Writing Game

University Of Massachusetts Amherst, Amherst MA

Investigators

Abstract

Developing “21st century skills” such as collaboration, communication, critical thinking, and creativity (the 4Cs) has become increasingly important for students to keep up with the ever-evolving labor market of the future. Teaching the 4Cs effectively and efficiently requires deeply intertwining them with core content knowledge areas, since the acquisition of domain knowledge can bolster students’ development of these soft skills. In this project, the investigators take a step towards combining 4C skill development with STEM education by developing a collaborative writing game in which multiple students work together to craft a narrative around embedded STEM education elements. As a key innovation, the investigators will embed this collaborative writing game with natural language processing and artificial intelligence (AI)-based tools to automate fact-checking, feedback, knowledge tracing, and narrative story arc suggestions, which will facilitate students’ progress toward mastery while reducing teacher workload. Overall, this project has the potential to increase student engagement in STEM learning activities and improve learning outcomes. The project will be grounded in StoriumEdu, a collaborative story writing platform, therefore directly benefiting its user base of 2,000 K-12 classrooms with over 27,000 students and potentially an even larger number of students through the dissemination of the team’s research findings. This major technical goals of this project are intended to augment scientific writing instruction with AI-based tools. To achieve these goals, the project will develop novel technologies that automatically provide writing assistance and feedback, and these tools will be deployed into K-12 classrooms via the StoriumEdu platform in order to evaluate their effectiveness. A core technical challenge is to assess the factuality of student writing by building machine learning models for fact-checking. The team proposes to design retrieval-augmented neural networks that can localize spans within student-written text that exhibit scientific misunderstandings. These spans will then be connected with relevant passages from textbooks or online articles to enable students to easily correct their errors. After developing fact-checking methods, the team will also focus on knowledge tracing, which allows measuring student progress over time in terms of which concepts they have mastered or are still struggling with. The knowledge tracing models will be developed with feedback from scientific literacy experts. The output of these models informs the final aspect of this project, which aims to generate narrative progressions associated with conceptual misunderstandings. This will allow students to engage more strongly with concepts that they have yet to master, which maximizes the writing platform’s pedagogical potential. Taken as a whole, this project’s research contributions synthesize novel NLP methods with educational progress tracking and feedback systems in an effort to improve STEM learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.