Background and Rationale:
As AI reshapes industries, educators must develop AI literacy to adapt. A survey by Teach Plus found that while 92% of Illinois educators see AI’s potential, only half have received AI training. Similarly, Samsung Solve for Tomorrow reported that 88% of parents believe AI knowledge is crucial for their children, yet many are unsure if schools teach AI. These findings reveal a demand for AI education but a lack of resources.

Many educators are skeptical about AI due to limited understanding and exposure. Concerns include ethical risks, student data privacy, and AI’s impact on teaching roles. Addressing these concerns requires a structured approach to evaluating AI literacy and the resources needed for AI adoption.

Research Objectives:
- Assess educators’ current AI knowledge.
- Identify common misconceptions about AI in education.
- Explore ethical concerns surrounding AI.
- Investigate systemic barriers to AI adoption.
- Recommend AI-focused professional learning programs.

Methodology:
A stratified random sampling approach ensures representation across different educator demographics, teaching levels, and geographic locations. The needs assessment survey includes Likert-scale, multiple-choice, and open-ended questions to provide both quantitative and qualitative insights. Questions focus on educators’ AI familiarity, confidence in using AI tools, ethical concerns, and the support structures needed for AI adoption. The survey is distributed through professional educator networks, district partnerships, and education technology communities over six weeks.

Preliminary Findings:
Early responses highlight several key themes. Many educators report minimal AI training, emphasizing the need for structured professional development. Ethical concerns—such as student data privacy and bias in AI-driven assessments—are frequently mentioned. Systemic barriers, including a lack of institutional support, limited training time, and scarce AI-integrated curricula, pose further challenges. Despite these obstacles, most respondents express strong interest in AI training, particularly in practical classroom applications and ethical considerations.

Discussion and Implications:
Findings offer insights into how AI literacy programs should be designed. To help educators integrate AI, training must address technical skills and classroom applications. Schools must also support AI adoption through clear policies, administrative backing, and accessible training. These findings will inform the next phases of this project, shaping AI literacy programs aligned with real-world teaching needs.

By focusing on educators’ perspectives, this research ensures AI literacy initiatives are practical and relevant. The results will guide professional development efforts, helping educators navigate the evolving role of AI in education.

Conclusion:
AI has the potential to transform education, but without proper training, educators may struggle to use it effectively. This study provides a critical first step in understanding AI literacy gaps and challenges. The findings from this needs assessment will help design AI-focused professional development programs that equip educators with the skills they need. This research contributes to the broader discussion on AI ethics, digital equity, and the future of teacher training in an AI-driven educational landscape.

Keywords: AI Literacy, AI in Education, Teacher Training, AI in Community.