An online, self-paced AI literacy professional development module designed for non-technical youth educators successfully addresses the critical gap between educators' desire to teach AI concepts and their lack of training, by prioritizing accessibility, comprehensibility, and transferable classroom-ready content across three progressive learning themes.
Objective
The primary objective of this study was to develop and pilot an online professional development module titled "AI Foundations and Applications: A Youth Teaching Guide" to address the significant training gap facing K-12 educators who wish to introduce artificial intelligence concepts to students but lack technical backgrounds and adequate resources. The module was designed specifically for youth educators, particularly non-technical teachers working with students aged 13-18 (grades 7 and above), through Nebraska's 4-H extension program. The research aimed to explore what insights could be drawn from piloting this self-paced AI foundations module about its potential to prepare educators to introduce AI concepts to youth. Upon successful completion, participants earn a digital badge demonstrating readiness to teach AI fundamentals and applications, positioning the module as both a training tool and a credential that validates educator competence in AI literacy instruction.
Methods
The researchers employed a case study design centered on developing and piloting a professional development module through the University of Nebraska-Lincoln's 4-H extension program and Online Education platform as a micro-credential badge. The module development was guided by three core design principles: (1) accessibility—lowering barriers for educators without computer science, statistics, or data science backgrounds through plain language, limited jargon, and visual graphics; (2) comprehensibility—ensuring easy-to-understand content through multiple mediums including introductory texts, educational videos, supplementary notes, reflection prompts, and hands-on activities; and (3) transferable, ready-to-use content—providing practical classroom-ready materials educators can implement immediately with their students.
The curriculum was structured around three sequential core themes mirroring natural technology learning progression: Theme 1: "What is AI and How Does it Work?" (covering definitions, capabilities, AI training, and generative AI mechanics); Theme 2: "Hands-on Use and Exploration" (facilitating experimental learning through prompt engineering, AI as collaborator/creative assistant/learning coach, business applications, and online influence); and Theme 3: "Ethics, Bias, Safety, and Digital Responsibility" (addressing bias, privacy, misinformation, safety guardrails, and trust). Each theme defined 3-5 learning objectives utilizing Bloom's Taxonomy, explicitly incorporating verbs from different cognitive levels to ensure progression from foundational knowledge (remember, understand) to higher-order thinking (analyze, evaluate, create).
Each lesson within the themes followed a consistent structure: introductory paragraph, curated educational videos from reputable YouTube sources (CrashCourse, IBM Technology, Code.org, Google for Developers), video takeaway notes with supplemental information, thought-provoking reflection prompts, a detailed classroom-ready activity with minimal material requirements and timing suggestions, and a four-question multiple-choice comprehension quiz requiring minimum scores to advance. Activities emphasized collaborative group work with physical materials (markers, poster paper) over individual computer work, and when AI tools were incorporated, youth-friendly platforms like Google's Teachable Machine or Canva AI were recommended. The module was offered both as a complete package and as standalone theme courses for flexibility. The module underwent review by a team of 4-H experts who lead learning experiences before publication on UNL's platform.
Key Findings
While the paper's pilot implementation and feedback sections remain incomplete in the provided document, the completed portions reveal several significant design achievements and theoretical contributions. The module successfully operationalized the historical lesson from calculator integration in the 1970s, when 84% of teachers wanted to use calculators but only 3% had adequate training or support resources. The researchers explicitly designed their module to prevent repeating this pattern with AI, where current data shows 58% of K-12 teachers have received no AI training (April 2025) and 70% have received no professional development for AI, with lack of training and insufficient resources identified as the largest hurdles to classroom AI integration.
The module's design principles directly address educator concerns documented in literature, including attitudes of uncertainty heightened among non-technical educators who perceive AI as complex with "magic-like qualities." By deliberately limiting technical jargon, providing plain-language definitions for new AI terms, and incorporating visual graphics (such as nested diagrams showing relationships between AI, machine learning, deep learning, and generative AI), the module makes AI concepts approachable rather than intimidating. The variety of content formats—text, video, notes, reflections, activities, quizzes—accommodates different learning styles and ensures educators don't merely consume material for badge completion but genuinely internalize content for future teaching.
The transferable content principle manifests in practical elements including detailed activity instructions with outlined materials, steps, and discussion prompts enabling seamless classroom facilitation. Reflection prompts and quiz questions double as instructional material educators can adapt for youth audiences. This design closes the gap between theory and practice, providing educators with a collection of classroom-ready resources to complement conceptual understanding. The learning objectives' alignment with Bloom's Taxonomy ensures cognitive progression from basic comprehension to advanced critical thinking and creative application across the three themes.
The module's focus on grades 7 and above reflects a strategic decision based on the increasing independent use of technology for schoolwork at this stage (online assignments, essays, presentations, research) and students' growing exposure to formal software (Microsoft Office, Google Suite, search engines) and AI tools. This positioning targets a pivotal developmental period when students can benefit most from structured AI literacy instruction that combines technical understanding with ethical awareness. The partnership with Nebraska's 4-H, which serves learners aged 8-18 through afterschool programs, camps, school enrichment, and special-interest workshops, provided both expertise review and a credible platform for dissemination beyond traditional formal education settings.
Implications
This research presents important implications for teacher professional development, curriculum design, and educational policy in the AI era. For teacher education programs and professional development providers, the study demonstrates that effective AI literacy training for educators requires more than technical skill-building—it must simultaneously address conceptual understanding, pedagogical application, and ethical considerations while remaining accessible to non-technical audiences. The three-pillar design framework (accessibility, comprehensibility, transferable content) offers a replicable model for developing professional development in emerging technologies that could be adapted beyond AI to other complex educational innovations.
For K-12 schools and districts, the findings underscore the urgency of providing systematic AI literacy training given that the majority of teachers lack such preparation despite AI's rapid integration into students' lives. The module's self-paced online format addresses practical constraints (time, scheduling, geographic distance) that often prevent teachers from accessing professional development. The digital badge credential provides a tangible validation of competency that schools can recognize when making decisions about curriculum integration and teacher assignments related to AI instruction.
For informal education providers (afterschool programs, educational camps, community organizations), the module demonstrates how AI literacy can be introduced through engaging, hands-on activities emphasizing collaboration over individual screen time. The focus on youth-friendly AI tools and activities requiring minimal technological infrastructure makes AI education accessible in resource-constrained settings, potentially helping bridge digital divides rather than widening them.
The module's progression from foundational concepts (Theme 1) through hands-on exploration (Theme 2) to ethical considerations (Theme 3) models a pedagogically sound sequence that educators can replicate when teaching youth. This mirrors natural technology learning progressions identified in educational technology adoption literature and aligns with constructivist learning theory by building knowledge incrementally with each lesson and theme scaffolding upon previous learning.
The emphasis on ethics, bias, safety, and digital responsibility as a complete theme rather than an afterthought signals that responsible AI use must be central rather than peripheral to AI education. By positioning ethical considerations as the culminating theme after students have gained technical understanding and hands-on experience, the module ensures educators can facilitate nuanced discussions grounded in practical knowledge rather than abstract principles.
Limitations
Several limitations warrant consideration. The pilot implementation and feedback sections remain incomplete in the provided document, meaning empirical evidence of the module's effectiveness, educator satisfaction, and actual implementation challenges has not yet been fully reported. Without pilot data, claims about the module's success in preparing educators remain theoretical rather than evidence-based. The module's focus on grades 7 and above excludes younger learners (ages 8-12 in the 4-H target range), potentially missing opportunities to build foundational AI literacy earlier in students' educational trajectories when attitudes and understanding about technology are forming.
The reliance on curated YouTube videos from external sources (rather than custom-developed content) creates sustainability concerns if videos become unavailable, outdated, or if platform algorithms change discoverability. While reputable sources were selected, the researchers do not control content updates or platform changes that could affect module coherence over time. The module's development through a single institution (University of Nebraska-Lincoln) in a specific geographic context (Nebraska) may limit generalizability to educators in different regions with varying resources, student demographics, and institutional support structures.
The self-paced online format, while addressing accessibility barriers, lacks the real-time support, peer interaction, and instructor feedback that face-to-face professional development provides. Educators encountering confusion or questions must rely on static materials rather than dynamic dialogue. The digital badge credential, while providing validation, has uncertain recognition and value across different school systems, potentially limiting its impact on encouraging participation. The focus on non-technical educators is appropriate given the training gap but may mean the module lacks sufficient depth for educators with technical backgrounds seeking more advanced AI pedagogy preparation.
Future Directions
Future research should complete and publish the pilot implementation findings, including quantitative data on completion rates, quiz performance, and satisfaction ratings, alongside qualitative feedback about perceived preparedness to teach AI concepts, confidence changes, and implementation challenges. Longitudinal studies tracking educators who completed the module would reveal whether they actually integrate AI instruction in their classrooms, what barriers emerge during implementation, and how student learning outcomes compare to students whose teachers lacked AI literacy training.
Expansion of the module to address younger age groups (elementary and early middle school) would create a comprehensive K-12 AI literacy pathway, with developmentally appropriate content for different cognitive stages. Research comparing online self-paced formats with hybrid or fully in-person professional development models would illuminate which delivery methods produce better educator preparation, retention, and classroom implementation. Studies examining the module's effectiveness across different educational contexts (urban versus rural, well-resourced versus under-resourced schools, different geographic regions) would establish generalizability and identify necessary adaptations.
Investigation of how different educator characteristics (teaching experience, subject area, prior technology comfort, personal AI use) influence module completion, comprehension, and subsequent classroom implementation would enable more targeted recruitment and support strategies. Research examining whether earning the digital badge credential influences hiring, promotion, or professional recognition decisions would establish the credential's practical value. Studies comparing educators who completed all three themes versus individual themes would reveal whether the integrated approach produces different outcomes than piecemeal participation.
Development of companion resources for students—modules or materials specifically designed for youth audiences that align with the educator training—would create a more complete AI literacy ecosystem. Research examining how educators adapt and modify the provided activities for their specific contexts would generate insights about flexibility, cultural responsiveness, and localization needs. Finally, periodic updates incorporating emerging AI technologies, evolving ethical concerns, and new pedagogical research would ensure the module remains current as the AI landscape rapidly evolves.
Title and Authors
Title: "From Questioning to Qualified: A Case Study of a Professional Development AI Literacy Module for Youth Educators"
Authors: Aditya Jain (Jeffrey S. Raikes School, The University of Nebraska-Lincoln) and Kimberly Stanke (4-H Youth Development, Department of Statistics, The University of Nebraska-Lincoln)
Published On: 2025 (specific date not provided in document)
Published By: Case study paper (publication venue not specified in provided document; appears to be submitted for conference or journal publication)