Integrating generative artificial intelligence in K-12 education: Examining teachers’ preparedness, practices, and barriers

University college of education

Idaho, USA

Researchers analyzing survey and qualitative responses

• In-school (K–12)

Online snowball-recruited questionnaire with mixed-methods analysis

Cross-sectional study window as reported

n = 89 teachers

ChatGPT, Gemini, DALL-E-class tools referenced

• Snowball sample limits representativeness
• Demographic skew (White, female, rural)
• Self-reported practice may differ from observation
• Policy landscape shifting post-survey

K-12 students served by respondents

Growing student GenAI use outside school

Varies; GenAI framed as natural-language tools

• Measure self-reported GenAI preparedness and integration
• Contrast in-class vs out-of-class GenAI use patterns
• Identify belief- and policy-related barriers

• Inform Idaho-specific support systems with US-wide relevance
• Align with “humans in the loop” federal framing

• Not a PD intervention outcome study
• Not student achievement experiment
• Not random district sampling

General-purpose GenAI assistants and multimodal generators

• Automation tool
• Co-creator

English-dominant US rural schools

• Heavy use for lesson prep, assessment drafting, admin
• Less frequent real-time classroom learning integration

• District policies for student data in cloud GenAI
• Pedagogical plans preserving human interaction
• Ethical guidelines for plagiarism and disclosure
• Risk-mitigation training to address value beliefs

• Recruit teachers via snowball
• Collect Likert and open-ended responses
• Integrate qualitative themes with quantitative patterns
• Derive policy and PD recommendations

• Teachers remain instructional leaders; GenAI supports backstage work when trusted

• Provide exemplar workflows for safe in-class GenAI
• Build local policy templates

• Underpreparedness despite hype
• Beliefs about risk and human interaction shape classroom avoidance
• Absence of clear guidance slows live integration

• Explicit comparison to prior general-edtech integration patterns
• Rural Idaho contextualization

• Documents realistic backstage-first adoption curve

• < half report integrating GenAI into educational practices; future intent may exceed current

Calls for targeted policies and support systems rather than assuming spontaneous classroom transformation.

• FERPA/COPPA-aware use of GenAI with student prompts
• Equity for rural bandwidth and paid-tool access
• Bias and inaccuracy communication
• Transparent disclosure when AI assists grading materials

• Post assessment
• Activity documentation
• Practitioner observation

• Randomized PD trials measuring shift to in-class GenAI use
• Student learning studies under clarified policies

• Teacher education
• Rural education
• GenAI policy

• Completed

K-12

Rural Idaho

GenAI backstage + emerging classroom

Survey-informed implementation science

Medium

Medium