Neelam Dwivedi

The ASEE Faculty Teaching Excellence Task Force is developing a three-level framework to support and document faculty members’ professional development in teaching, with NSF support, at eight diverse colleges of engineering across the United States. Those schools are The College of New Jersey, Lafayette College, Northern Arizona University, University of Bridgeport, University of Georgia, University of Nebraska-Lincoln, The University of the Pacific, and Worcester Polytechnic Institute. https://lnkd.in/eWnjvuFB

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In the fourth talk of Programming Education session at #AIED2025 Hasnain Heickal presents a joint work with Andrew Lan "Learning Code-Edit Embeddings to Model Student Debugging Behavior" - excellent idea and a nice study. You could read the paper here: https://lnkd.in/e6HjBzFb

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To close this stream, here is another interesting talk from the last Programming Education session at #AIED2025. Martha Shaka presented "Personalised Code and Error Predictions in Programming Education via Large Language Models" - an interesing attempt to generate problem solution "in a student" way, closer in the structure (and errors) to what the real student will produce. Joint work with Diego Carrara and Kenneth Brown. Read it here https://lnkd.in/eqF6BuFs

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If you haven't heard about the Scholarly Graph yet, you should check it out! Super fascinating stuff. EBSCO's linked data experts are passionate about making research more connected — linking over 900 million scholarly entities and 3.8 billion relationships to uncover hidden connections and trace the story of scholarship. It's starting to power Citation Discovery features in EBSCO Discovery Service and EBSCOhost — and that is just the beginning! Learn more: https://ow.ly/cu8Z30sQe1I

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What Has NSF Funded Regarding Using AI in 2025 Regarding K-12 STEM Education You will find below a listing of NSF project titles and project numbers for STEM research involving AI in some way. To keep in the spirit of AI, I asked an LLM to identify the broad research themes (as you can see in the graphic). Nothing mind-blowing, but maybe it will take your human brains to use AI in education to "infinity and beyond." Knock yourself out. You can look up an abstract, PI names, etc. at nsf.gov in award search. Using Large Language Models to Support Authoring Intelligent Tutoring Systems 2506957 Leveraging AI Innovation to Enhance School-Wide Literacy Through Active Learning in Montgomery County, North Carolina, Elementary Schools 2524505 Supporting Students' Modeling Practice Through a Large Language Model-Powered Pedagogical Agent in an Augmented Reality Learning Environment 2506890 Engaging K-8 Teachers in Integrating Computer Science, AI, and Cybersecurity into Standard-Based Teaching 2509044 Accelerating Professional Learning Through an Intelligent Performance Assessment System 2506438 Using Large Language Models to Support Authoring Intelligent Tutoring Systems 2506957 Leveraging AI Innovation to Enhance School-Wide Literacy Through Active Learning in Montgomery County, North Carolina, Elementary Schools 2524505 Virtual Reality and Artificial Intelligence for Personalized Learning in Sustainable Construction Education 2507003 Accelerating Professional Learning Through an Intelligent Performance Assessment System 2506438 Building Data Fluency and Generative AI Literacy Through Data Adventures 2509929 Exploration and adaptation of experience sampling to understand the impact of artificial intelligence tools on the work of teaching 2500101 Using Generative AI to Create Illustrative Diagrams to Support Mathematical Problem-Solving 2507009 Research Practice Partnership Development to Integrate AI Literacies in K-8 2509528 Exploration and adaptation of experience sampling to understand the impact of artificial intelligence tools on the work of teaching 2500101 Harnessing Student Reflections with AI-Driven Personalization in Large STEM Lectures 2507035 Building AI Models to Help Middle School Students Interpret Science Diagrams 2506945 Hybrid symbolic and generative AI-based simulations of classroom discussions for improving teacher facilitation skills 2506474 Building Data Fluency and Generative AI Literacy Through Data Adventures 2509929 Scaling Up AI Education for Rural Upper Elementary Students with Immersive Problem-Based Learning 2445720 Exploration and adaptation of experience sampling to understand the impact of artificial intelligence tools on the work of teaching 2500101 Using Generative AI to Create Illustrative Diagrams to Support Mathematical Problem-Solving 2507009

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The case for Open Access Publishing via Tuwhera and mention of PJTEL https://lnkd.in/gw6eTa4y The Pacific Journal of Technology Enhanced Learning https://lnkd.in/gRzS2s3w aims to provide new and emerging, as well as experienced researchers an open access platform to share their findings and discourse. By Donna Coventry & Luqman Hayes

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✨Our work is featured on the Massachusetts Institute of Technology News homepage spotlight today! 🔗 https://lnkd.in/dgd6nBi4 At its core, this research tackles a simple but powerful idea: engineering design must embrace uncertainty, not ignore it. Traditional methods rely on best- and worst-case bounds — but in the real world, systems like autonomous vehicles, UAVs, and future mobility networks face probabilistic risks, trade-offs, and interdependent uncertainties. In our recent paper, "On Composable and Parametric Uncertainty in Systems Co-Design", we extend our monotone co-design framework to reason about uncertainty in a unified and modular way: 📊 Moving beyond intervals to capture distributions and risk trade-offs 🧩 Preserving compositionality, so subsystems (e.g., sensors, actuators, batteries, algorithms) can be combined seamlessly. We do this via applied category theory! 💥 🚁 Demonstrating through a case study on UAV design how uncertainty-aware methods can reveal insights invisible to deterministic models (e.g., when a design is infeasible with 12.8% probability vs. being simply “worst-case infeasible”) This is part of a broader vision: developing tools that allow engineers to design complex systems with rigor, transparency, and adaptability — even under uncertainty. Great work with my students Yujun Huang and Marius Furter, and the support of MIT Civil and Environmental Engineering, MIT Laboratory for Information and Decision Systems (LIDS), and MIT Institute for Data, Systems, and Society (IDSS) #MIT #Research #SystemsDesign #Uncertainty #Autonomy #CoDesign #Engineering

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Improving knowledge tracing for learning to program with srcML-based code representations, making it possible to better model what students know even when they produce unparseable code. https://lnkd.in/e26_zh9u w/ Maciej Pankiewicz, Yang Shi

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Computing Talent Initiative partnered with UR2PhD to offer a self-paced, asynchronous research course specifically designed to help students "try before they commit" to a full research experience. We wrote about our experience offering the course and its early outcomes, including a number of students receiving REU (Research Experience for Undergraduates) offers, please check it out: https://lnkd.in/g4yxJknE #ComputingResearch #HigherEd #STEM

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