In many courses, “do your own research” assignments are intended to build scientific identity—but they can unintentionally amplify barriers for learners who experience executive function challenges, anxiety, or uncertainty in open-ended tasks. This session shares an open, adaptable assignment sequence that pairs open science platforms (e.g., Zooniverse, Figshare, Zenodo, public datasets) with genAI as a learning partner for planning and task initiation—without positioning AI as a writing shortcut. Participants will see the midterm-to-final scaffold (planning roadmap → multimodal presentation), examples of student pathways (citizen science, dataset modeling, usability evaluation), and strategies for keeping AI use optional, transparent, and ethically grounded. Attendees will leave with reusable prompts, learning outcomes, and an implementation checklist to support accessible, authentic inquiry in their own disciplinary context.
Attendees of this session will be able to:- Identify and evaluate an open science resource or dataset for use in a course-based inquiry assignment.
- Design a scaffolded student research “on-ramp” that supports planning, task initiation, and iterative exploration.
- Integrate AI tools responsibly as optional learning partners for brainstorming and organization rather than automated writing.
- Create multimodal deliverables (e.g., flexible presentation formats) that emphasize process, reflection, and transparent sourcing.
- Apply inclusive, UDL-aligned choices that improve access for diverse learners while preserving authentic inquiry.
