K-12 educators express strong interest in AI integration but lack adequate training, with 74% citing insufficient professional development as the primary barrier—highlighting an urgent need for role-specific, hands-on AI training programs.
Objective: The primary goal of this study was to conduct a comprehensive needs assessment to evaluate K-12 educators' awareness, understanding, utilization, and concerns regarding artificial intelligence (AI) technologies in education. Specifically, the research aimed to identify the professional development needs of teachers, instructional support staff, and administrators to support effective and ethical AI integration in K-12 classrooms and schools.
Methods: The study employed a survey-based research design to collect data from 438 K-12 educators across 34 school districts in Northeast Ohio. The participant pool included 316 teachers (72.1%), 54 instructional support staff (12.3%), and 68 administrators (15.6%). Data collection occurred through multiple channels including workshops conducted by educational service centers, regular meetings with superintendents and coordinators, online invitations to affiliated school districts, and individual teacher-shared invitations beginning in October 2024. The comprehensive survey instrument contained 37 questions organized into five main categories: level of technology-related knowledge and skills, awareness and understanding of AI technologies, current usage of AI technologies in teaching, concerns about AI technologies, and professional development needs for AI technology. Responses were measured using 5-point Likert scales and multiple-choice options. Descriptive statistical methods were employed for preliminary analysis to examine educators' self-perceived understanding, awareness, professional development requirements, and ability to model appropriate AI use for students.
Key Findings: The study revealed several critical findings about AI integration in K-12 education. First, regarding AI familiarity and confidence, educators demonstrated strong general technological proficiency, with 55% of teachers rating themselves at level 4 for technology knowledge. However, there was a significant gap when it came to AI-specific competencies. Only 3.6% of teachers, 18.5% of instructional support staff, and 2.9% of administrators rated their AI familiarity at the highest level. The majority of educators placed themselves at moderate levels of AI understanding, with many expressing limited exposure and insufficient hands-on experience. This lack of familiarity directly impacted confidence levels, with educators feeling considerably less confident using AI technologies compared to their overall comfort with general technology adoption.
Second, concerning AI usage patterns, a substantial number of educators reported minimal engagement with AI tools in their classrooms. Specifically, 12% of teachers reported never using AI, 23.7% rarely used it, and 12.66% used it only once per month. The largest group (29.11%) used AI at least once per week, while 22.47% incorporated it daily. Similar patterns emerged among instructional support staff and administrators, suggesting that limited practical experience contributes to lack of familiarity and confidence.
Third, regarding concerns about AI integration, educators across all roles identified student over-reliance on AI and potential loss of learning skills as their most significant concern, cited by 75% of teachers, 79% of instructional support staff, and 85% of administrators. Other major concerns included inadequate teacher preparation (72% of instructional support staff, 46% of administrators), ethical implications of AI use (49% of teachers, 46% of instructional support staff, 54% of administrators), data privacy and security risks (39-48% across groups), and lack of transparency in understanding how AI technologies operate (33-45% across groups).
Fourth, concerning barriers to AI adoption, the lack of professional training in AI technologies emerged as the most significant challenge, reported by 74.2% of teachers, 63.3% of instructional support staff, and 79.1% of administrators. Time constraints constituted another major barrier, cited by 63% of teachers, 42.4% of instructional support staff, and 56.7% of administrators. Limited access to AI technology was noted by approximately 31% across all groups, highlighting ongoing equity gaps in technological infrastructure. Additional barriers included resistance to change, lack of clear implementation guidelines (especially among administrators at 62.7%), and cost concerns (more pronounced among administrators at 34.3%).
Fifth, regarding professional development experiences and preferences, a substantial percentage of educators reported not receiving any formal AI training—68.35% of teachers, 59.3% of instructional support staff, and 73.5% of administrators. Among those who had received training, satisfaction levels were moderate at best, with administrators showing the lowest overall satisfaction (37% reported no satisfaction at all). When asked about preferred training formats, educators overwhelmingly favored hands-on practice with AI tools (82-91% across groups), followed by workshops or training sessions (78.7-80.8%), and collaborative learning communities (42.9-53.8%). Regarding training content preferences, practical demonstrations and guided practice with AI tools commonly used in K-12 education ranked highest (72% of teachers, 76% of instructional support staff, 65% of administrators), followed by practical applications of AI in schools (70% of teachers), and foundational knowledge about AI and machine learning basics (66% of teachers, 64% of instructional support staff, 42% of administrators).
Finally, educators' perceptions of AI revealed nuanced attitudes. When asked to provide five words describing AI, the most frequently mentioned terms were "Help," "Cheat," "Scary," and "Use," reflecting mixed feelings of optimism, concern, and uncertainty. Interestingly, while "Help" dominated among teachers and administrators, instructional support staff more commonly used "Helpful," suggesting their greater familiarity with AI capabilities. Despite limited knowledge and concerns, educators demonstrated strong interest in learning AI integration, with approximately 54.9% of teachers, 64.7% of instructional support staff, and 76.1% of administrators expressing high interest (levels 4 and 5) in using AI for teaching and administrative purposes.
Implications: The findings have significant implications for AI integration in K-12 education. First, they underscore the critical need for comprehensive, role-specific professional development programs that address the distinct responsibilities of teachers, instructional support staff, and administrators. Teachers require training focused on classroom implementation and instructional innovation, instructional support staff need strategies for coaching educators and facilitating AI tool integration, while administrators require training in strategic planning, program evaluation, and school-wide adoption. Second, the study highlights the importance of addressing ethical considerations in all AI training programs, ensuring educators can navigate issues related to data privacy, algorithmic bias, fairness, and responsible use. Third, the findings reveal that simply offering training is insufficient—professional development must be practical, discipline-specific, flexible, embedded in real contexts, and sustained over time to ensure meaningful impact. Fourth, the research emphasizes the need for systemic support including equitable access to AI infrastructure, clear implementation guidelines, adequate time allocation, and strong leadership backing. Finally, the study demonstrates that despite feeling unprepared, educators recognize AI's importance and express strong desire to build their AI competencies, presenting an opportunity for educational institutions to invest in targeted capacity-building initiatives.
Limitations: The study acknowledges several limitations. First, the sample was geographically limited to 34 school districts in Northeast Ohio, which may limit generalizability to other regions or contexts with different demographic, socioeconomic, or technological characteristics. Second, the study relied on self-reported survey data, which may be subject to social desirability bias or inaccurate self-assessment of skills and knowledge. Third, the cross-sectional nature of the data collection provides only a snapshot of educators' perceptions and needs at a specific point in time, without capturing how these may evolve as AI technologies and educational practices develop. Fourth, the preliminary analysis employed primarily descriptive statistical methods, which limits the ability to identify causal relationships or predictive factors influencing AI adoption. Fifth, the study focused on educators' perspectives without directly examining student experiences, outcomes, or perspectives on AI use in classrooms. Finally, the research did not investigate actual implementation practices or measure the effectiveness of specific AI integration approaches, focusing instead on perceived needs and barriers.
Future Directions: The study outlines several important directions for future research. First, longitudinal studies are needed to examine the long-term impact of role-specific AI professional development on educator efficacy, instructional practices, and student outcomes, tracking how teachers, instructional support staff, and administrators apply AI training in real educational settings over extended periods. Second, research should investigate students' perspectives on AI use in classrooms, particularly regarding their learning experiences, ethical awareness, digital agency, and potential over-reliance concerns. Third, comparative studies across different school districts, grade levels, states, or countries could identify contextual factors that influence successful AI integration and inform scalable policy and practice. Fourth, as AI technologies evolve rapidly, ongoing research should examine how emerging tools reshape teaching and learning, and how professional development can remain adaptive and future-focused. Fifth, experimental or quasi-experimental studies could evaluate the effectiveness of different training models, formats, and content approaches in building educator confidence and competence. Sixth, research is needed on equitable access to AI technologies and professional development, examining how to address disparities across schools with varying resources. Finally, studies should explore the development and validation of AI literacy frameworks and competency standards for K-12 educators across different roles and subject areas.
Title and Authors: "AI Integration in K-12 Education: Comprehensive Needs Assessment of Teachers, Administrators, and Instructional Support Staff" by I-Chun Tsai, Associate Professor at the University of Akron.
Published on: The article was presented at eLearn 2025, scheduled for October 13-16, 2025, in Bangkok, Thailand.
Published by: eLearn 2025 Conference Proceedings.