The program unfolded in two phases at a computer science outreach initiative hosted at a Historically Black College and University in the southeastern United States. During a three-and-a-half-week Summer Academy, students received twelve hours of foundational AI instruction layered onto broader computer science training that included block-based programming in MIT App Inventor. The lessons were built around Ng and colleagues’ four-part AI literacy framework, which moves learners from knowing and understanding AI, through using and applying it, to evaluating and creating AI-supported solutions, all while embedding ethical reasoning. Students distinguished AI from non-AI systems, explored supervised versus unsupervised machine learning, compared outputs from ChatGPT, Gemini, and Perplexity, practiced prompt refinement, and generated images with Adobe Express while discussing algorithmic bias, hallucinations, deepfakes, and the environmental footprint of large AI systems.

In the fall phase, fifteen students who completed the full experience worked in small groups for four months to design mobile apps addressing problems relevant to their peers or local communities. Each team was required to propose AI-enabled features that extended app functionality, and they were explicitly encouraged to engage with a generative AI tool of their choice as a collaborative partner throughout ideation, research, wireframing, prototyping, and coding. The project culminated in a face-to-face app competition before a panel of judges, in which students had to justify the AI components of their designs and explain how those components would function for real users.

The design artifacts and presentations revealed that participants had internalized emerging AI concepts in practical form. Across the teams, students proposed recommendation systems, chatbots, content generation tools, personalized tutoring systems, and information verification tools aligned with authentic user needs. One team, Trash to Treasure, designed an app promoting the recycling and repurposing of old compact discs, with AI features that generated personalized upcycling ideas and offered chatbot guidance on recycling decisions. Another, EduQuest, built a recommendation system that analyzed survey responses to suggest universities, housing, and employment opportunities to applicants navigating the college process. A team called Artik tackled misinformation in online artistic communities with proposed fact-checking tools, while Inspiring Bright Mind envisioned an educational app providing age-appropriate AI tutoring and custom instructional materials for teachers.

Interview data showed that students treated AI outputs as raw material for judgment rather than finished products. One tenth-grade participant described her team’s process of generating app name candidates with AI and then reshaping them until something original emerged. Another ninth grader explained how she prompted the system to analyze what users loved and hated about Pinterest, then used that synthesized feedback to guide her team’s design decisions. Students also reported using generative AI to create logos, produce visual assets, and obtain programming advice, consistently describing a cycle of evaluation, modification, and improvement rather than simple acceptance of machine output. The researchers interpret this pattern as evidence of collaborative human-AI interaction, in which the AI contributes ideas while students retain responsibility for evaluation and decision-making.

The quantitative picture was more complicated. On a twenty-item knowledge assessment adapted from the validated AI Literacy Concept Inventory, average scores rose only modestly, from 13.14 before the program to 14.10 afterward, a difference that was not statistically significant. Individual trajectories varied widely: roughly 57 percent of students improved, about 20 percent declined, and the rest showed little or no change. Some students articulated striking conceptual growth, with one participant reporting that she had previously thought AI was just Siri and ChatGPT and now understood everything from personalized advertisements to machine vision that identifies weeds in fields. Others, including one student whose scores fell, could not articulate core ideas such as algorithmic bias when surveyed afterward. The discrepancy suggests, the authors argue, that students’ learning showed up more strongly in their applied design practices than in multiple-choice test performance, underscoring the limits of traditional knowledge measures for assessing AI literacy in diverse learners.

Perceptual measures were similarly nuanced. Composite self-efficacy, intrinsic motivation, and career interest scores shifted only slightly and without statistical significance, although students reported strong perceived gains in professional skills on a follow-up survey, with mean scores above 4.45 on a five-point scale. Collaboration skills drew the highest ratings, including working effectively with others and communicating ideas, consistent with a substantial body of research showing that sustained project-based design work cultivates transferable workplace competencies. The researchers note these are self-reports rather than objective behavioral measures, but the pattern suggests that AI-integrated app design may build both AI-specific competencies and broader professional skills simultaneously.

Perhaps the most revealing findings concerned the instructional challenges that surfaced. Students arrived with widely varying technological readiness; some struggled with basic file management and data uploads, and several admitted they relied almost entirely on smartphones, leaving them inexperienced with the sustained keyboard-based text input that generative AI tools demand. Attitudes toward AI split dramatically. On one end, a team fabricated a needs assessment report with AI instead of collecting genuine data, a misuse they acknowledged and corrected after the fact. On the other end, some students avoided AI entirely for fear of academic punishment. One tenth grader explained that her school monitored iPad use and forbade AI, so she refused to touch it, then reversed course after trying ChatGPT during the program, only to abandon it again when a teammate’s writing was flagged as machine-generated. The study also found that competing software development demands crowded out deeper AI engagement, and that the sheer breadth of AI content compressed into limited instructional hours left some students struggling to consolidate their understanding.

The authors draw three principal implications for educators. First, generative AI should be positioned as a collaborative partner in project-based learning rather than merely a task-completion tool, giving students structured opportunities to ideate, refine, and solve problems alongside AI systems they must learn to evaluate critically. Second, instruction must be differentiated and scaffolded, prioritizing foundational digital skills such as keyboarding alongside tiered AI challenges matched to students’ prior knowledge, a need that is especially acute for students from communities historically excluded from computing education. Third, short bursts of exposure are insufficient; meaningful AI literacy requires sustained, curriculum-embedded time for iterative practice, reflection, and ethical engagement. The study also raises a subtler tension for the field: curiosity about new AI technologies actually declined slightly among participants, possibly because lessons on bias, environmental costs, and hallucinations replaced early enthusiasm with a more critical, realistic view. The researchers caution that their small, single-site sample, participant attrition from 36 initial participants to 15 completers, a middle-school-oriented assessment instrument, and the lead author’s dual role as instructor and researcher all limit generalizability. Future work, they argue, should develop performance-based and design-oriented assessments, explore longer curriculum-integrated programs, and examine how school policies shape whether students learn not just how to use AI, but when and why its use is appropriate.

Subject of Research: AI literacy development among high school students through generative AI-supported project-based mobile app design

Article Title: Cultivating AI literacy among high school students through generative AI as a collaborative partner

Article References: Hur, J. W., Bhuyan, J., Wu, F., Thomas, C., & Koong, K. (2026). Cultivating AI literacy among high school students through generative AI as a collaborative partner. Discover Education, 5(1), Article 805. https://doi.org/10.1007/s44217-026-02092-7

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02092-7

Keywords: AI literacy, generative AI, high school students, project-based learning, mobile app design, African American students, underserved communities, human-AI collaboration, instructional scaffolding, AI ethics, Discover Education, K-12 education

The scale of Ohio’s shortage of licensed behavioral health providers is difficult to overstate, particularly in rural and low-income communities. Residents in these areas may wait months for an appointment, travel long distances to reach a clinician, or go without care entirely during a crisis. The scholarship program is engineered to close that gap not simply by training more clinicians, but by training and retaining clinicians who are committed to working where the need is greatest. Every student who accepts a scholarship must sign a binding service agreement committing to work in an eligible underserved Ohio community for a period equal to the number of years they receive scholarship support. It is a model borrowed, in spirit, from loan-forgiveness frameworks in medicine, but applied here at the graduate level and targeted specifically at mental health and substance use treatment.

The qualifying employment settings read like a map of Ohio’s safety net. Scholars may fulfill their commitment at community behavioral health centers, federally qualified health centers, school-based health clinics, and child advocacy centers. They may also serve at behavioral health providers participating in OhioRISE, a specialized managed care program for youth with complex behavioral health and multisystem needs, or at nonprofit providers where Medicaid is the primary payer. By anchoring the service requirement to these settings, the state is directing its investment toward children, youth, and underserved populations — the groups most likely to fall through the cracks when the clinical workforce thins out.

At the University of Cincinnati, the program is a deliberate collaboration across three disciplines: social work, mental health counseling, and clinical psychology. The university will receive approximately $1.2 million in scholarship funding, and eligible graduate students may receive full in-state tuition coverage for fall 2026. Notably, tuition may be reimbursed retroactively after a student is accepted into the scholarship program, a provision that could deliver immediate financial relief to students who have already paid for the current term. Scholarships may also be eligible for renewal in spring 2027, fall 2027, and spring 2028, giving students a multi-year funding runway through the completion of their degrees.

The tuition scholarships are only part of the financial architecture. An additional $2.5 million is available through an independent agreement with Health Impact Ohio to fund $4,000 internship stipends paid directly to students during their field placements. Field placements — the supervised clinical practicums that graduate trainees must complete before licensure — are typically unpaid or minimally paid, forcing many students to work outside jobs while accumulating clinical hours. Direct stipends during placements remove one more economic obstacle between students and clinical training, and they signal that the state views the practicum year as work of genuine public value rather than a rite of passage to be endured.

For program leadership at UC, the timing is not incidental. Dana Harley, PhD, project director for the OBHS Program at UC, professor and master of social work program director, pointed to the changing federal landscape of graduate financing. Providing these scholarships will reduce the educational debt of students, she said, which is extremely important at a time when federal graduate student loans have been limited due to legislative changes. The remark underscores a structural tension in the behavioral health pipeline: clinical careers in community settings rarely pay enough to service heavy graduate debt, and when federal borrowing becomes less available, students from modest backgrounds may be priced out of the professions entirely. Scholarships that erase tuition, paired with placement stipends, are a direct countermeasure. We are delighted to train these future clinicians to provide services to some of the most vulnerable populations in Ohio, Harley said.

Eligibility is defined tightly. Accelerated, full-time, and part-time graduate students who are currently enrolled qualify, but incoming students do not — a design choice that channels the first wave of funding to students already embedded in Ohio’s training programs and closest to entering the workforce. Three UC programs are included. The Master of Social Work in the College of Allied Health Sciences integrates classroom learning with a field practicum, building clinical skills in assessment, intervention, and case management, and prepares graduates to pursue licensure as licensed social workers and licensed independent social workers in Ohio. The Master of Arts in mental health counseling, housed in the College of Education, Criminal Justice, Human Services and Information Technology, trains students to provide individual, group, and family counseling across a range of mental health and substance use concerns, grounded in culturally responsive, evidence-based practice, with supervised clinical practica that lead toward licensure as licensed professional counselors.

The third UC pathway is the doctorate in Clinical Psychology in the College of Arts & Sciences, which offers rigorous training in psychological assessment, evidence-based intervention, and applied research. Doctoral students complete supervised clinical hours across a range of populations and settings, culminating in an American Psychological Association-accredited internship before graduation, and emerge prepared for licensure as psychologists and for careers spanning clinical practice and academic research. Amanda La Guardia, PhD, professor and counseling program coordinator, noted that the clinical counseling program in the School of Human Services has a consistent record of interprofessional collaboration across campus, resulting in supportive grant funding for students as they study to become professional counselors throughout Ohio. That cross-campus coordination — social work, counseling, and psychology drawing on a single pool of state scholarship dollars — is itself a technical achievement, requiring aligned eligibility criteria, coordinated advising, and shared reporting to state agencies.

The application process is deliberately simple in structure. Current graduate students interested in the scholarship apply through their respective programs, and the application includes a personal statement explaining why the student is interested in working with Ohio populations from disadvantaged backgrounds. That requirement does more than filter for commitment; it frames the entire program around a question of vocation. The state is not merely buying clinical labor with tuition dollars — it is identifying students whose professional identities are already oriented toward underserved communities and removing the financial friction that might otherwise push them toward higher-paying private practice. If the model works, the returns compound: each scholarship year purchased is a service year delivered in a community behavioral health center, a school-based clinic, or a child advocacy center, precisely where Ohio’s shortage is sharpest. For a state confronting a behavioral health crisis with a thinning clinical workforce, the $18 million wager is that the fastest route to more care is not more buildings or more programs, but more people — trained, licensed, and rooted in the communities that need them most.

Subject of Research: A statewide Ohio scholarship program to expand and retain the behavioral health clinical workforce

Article Title: University of Cincinnati part of $18 million statewide initiative to expand behavioral health workforce

Article References: University of Cincinnati part of $18 million statewide initiative to expand behavioral health workforce. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: behavioral health, workforce development, Ohio Behavioral Health Scholarship, University of Cincinnati, mental health workforce shortage, social work, mental health counseling, clinical psychology, graduate scholarships, underserved communities, substance use treatment, OhioRISE