Workshop Themes
This workshop focuses on career learning and career ideation in the age of rapidly evolving AI, examining how AI tools may support or hinder students' exploration of interests, identities, and pathways across K–16 education, with a focus on STEM.
Topic I: Redefining Career Readiness in the Age of AI
How does AI reshape how STEM career readiness is conceptualized and assessed? We invite participants to examine how AI-mediated systems interact with developmental theories of STEM career exploration. Career readiness is multidimensional, encompassing attitudes (confidence, curiosity), knowledge (pathway awareness), and behaviors (exploration, planning). State and national frameworks similarly define readiness beyond content mastery. As AI literacy becomes embedded in workforce strategies, preparation for STEM careers may increasingly include technical fluency, ethical reasoning, and human–AI collaboration. We encourage contributors to consider whether AI systems narrow readiness to measurable performance signals or expand how it is theoretically defined and longitudinally assessed.
Topic II: AI Design Opportunities, Limits and Boundaries
What roles should AI play—and not play—in STEM career decision-making? AI can surface pathways, prompt reflection, and provide formative feedback. However, responsible systems should not function as deterministic sorting mechanisms based on early performance signals. AI at scale must expand option spaces rather than predict “fit.” Educators and advisors remain central in interpreting recommendations and supporting identity-relevant decisions. We invite discussion on governance and evaluation: What benchmarks define effective AI-supported career coaching? Should human advising serve as a gold standard? What data infrastructures are necessary to responsibly evaluate AI systems at scale while preserving student agency?
Topic III: K–16 Differentiation and Continuum
How should AI-supported STEM career exploration vary across developmental stages and institutions? Across K–16 education, AI-supported exploration must align with developmental readiness. Early grades may emphasize identity expansion and exposure; middle school may scaffold exploratory behaviors; secondary and postsecondary levels may support structured decision-making tied to workforce pathways. Because preparedness evolves iteratively, AI systems should shift from curiosity building to informed planning without hardening trajectories. At scale, responsible systems must remain adaptable across institutional missions, disciplinary cultures, and regional workforce contexts.
Topic IV: Equity, Access, and Inclusion Pathways
How can AI systems broaden participation rather than reproduce historical inequities in STEM? AI-driven career systems risk reproducing disparities embedded in historical data. Career development is shaped by contextual supports and access to opportunity, and perceptions of fairness vary across student populations. We encourage discussion of bias auditing, transparent recommendation logic, diverse representation, and educator mediation. Structural barriers in STEM—including those affecting students with disabilities—must also be considered; for example, deaf and hard-of-hearing learners often face cumulative access challenges in technical terminology. Responsible AI for STEM career development should operate alongside sustained, equity-centered pipeline interventions such as #GOALS.
Workshop Schedule
This 3.5-hour interactive workshop is organized around two complementary lenses: (1) understanding AI-enabled STEM career development across developmental stages and (2) proposing stakeholder-centered design solutions. During registration, participants will indicate their preferred developmental focus (middle school, high school, undergraduate) and stakeholder lens (students, families, educators/advisors, institutions/industry). Group assignments for Activity 1 will be assigned and adjusted by organizers to balance expertise and ensure interdisciplinary exchange where activity 2 will be based selected by attendees. All lightning presentations are pre-submitted and grouped by the organizing team.
Opening Lightning Presentations: Framing the Problem Space
Ten 4-minute presentations introduce key tensions in AI-supported career readiness, including shifting definitions of readiness, developmental differences, equity and access disparities, institutional constraints, and ethical boundaries.
Activity 1: Developmental Lens
Participants break into age-based groups (middle school, high school, undergraduate). Each group analyzes stage-specific career decisions, appropriate roles for AI support, developmental guardrails, and risks of over-automation. Groups produce three opportunity areas, three risks, and three design principles, and shared with the other groups.
Break
Activity 2: Stakeholder Lens
Participants reorganize into stakeholder-centered groups (students, families, educators/advisors, institutions/industry). Groups examine incentive structures, authority boundaries, AI literacy requirements, and power asymmetries. Each group generates five design recommendations, one red flag to avoid, and one urgent research question, and shared with the other groups.
Final Lightning Presentations: Design Solutions
Ten 4-minute presentations focus on AI-supported design approaches, governance strategies, and implementation models. These talks build directly on the workshop's analytical discussions and highlight concrete pathways forward.