Understanding K-12 Teachers’ Needs for AI Education: A Survey-Based Study

Source publication / research team or educational organization described in paper

USA

Researchers, educators, instructors, or facilitators as described in the source publication

• In-school (K-12)
• Higher education

Classroom, course, or resource-based AI education activity

Not specified in extracted text

Understanding K-12 Teachers’ Needs for AI Education: A Survey-Based Study Nazan Bautista, John Femiani, Daniela Inclezan Miami University Oxford, O; n of misconceptions and biases in the classroom. This study seeks to identify K-12 teachers’ self- identified needs regarding AI education and compare them with existing research on professional development (PD); research on professional development (PD) for AI integration. We surveyed 34 K-12 teachers to assess their knowledge of AI, identify areas where they require further support, and evaluate the relevance of curre

LLM/Chat

• Teacher readiness, time, support, and classroom integration may affect implementation quality.
• AI output reliability, hallucination, academic integrity, and age-appropriate use require safeguards.
• Use with minors requires attention to privacy, consent, data minimization, and adult supervision.

K-12, Higher education

Mixed or not explicitly specified; infer from target learner group and intervention design.

Varies by intervention; not specified unless the paper explicitly describes prerequisites.

• Document the AI education intervention, course, tool, or resource described in the source publication.
• Extract the learner context, AI role, pedagogy, outcomes, and constraints for AAB registry comparison.
• Understanding K-12 Teachers’ Needs for AI Education: A Survey-Based Study Nazan Bautista, John Femiani, Daniela Inclezan Miami University Oxford, OH 45056, USA nubautista@miamioh.edu, femianjc@miamioh.edu, inclezd@miamioh.edu Abstract With the rapid rise of AI

• Support AAB comparison across AI literacy, AI education, teacher training, higher education, and workforce contexts.
• Capture evidence maturity, transferability, and limitations rather than treating the publication as product endorsement.

• Not an AAB endorsement of the tool, curriculum, provider, or result.
• Not a direct replication record unless the source paper reports implementation details sufficient for replication.

LLM/Chat

Not specified in extracted text

• Learning object / concept model

• Primary interaction pattern inferred from publication: Curriculum / course design, Teacher professional development, Ethics / responsible AI education.
• AI capability focus: LLM/Chat.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.
• Require human review of generated outputs and explicit guidance against over-reliance or answer copying.
• Include bias, fairness, transparency, and social impact discussion as part of the learning design.

• Review the publication’s reported context, learner group, AI tool or curriculum, implementation process, and outcome evidence.
• Map the case to AAB registry fields for comparison across educational levels and AI capability types.
• Use the source publication and PDF for any manual verification before public registry release.

• Human educators/researchers remain responsible for instructional design, supervision, interpretation, and ethical safeguards.
• AI systems or AI concepts provide the learning object, support tool, evaluator, simulator, or automation context depending on the paper.

• Hands-on / experiential learning
• Registry extraction emphasizes explicit learning goals, observed outcomes, constraints, and safety limitations.

• Teacher readiness, time, support, and classroom integration may affect implementation quality.
• AI output reliability, hallucination, academic integrity, and age-appropriate use require safeguards.
• Use with minors requires attention to privacy, consent, data minimization, and adult supervision.

• Case classified under: Published survey study.
• Pedagogical pattern: Hands-on / experiential learning.
• Any additional adaptations should be verified against the full paper before public-facing publication.

• Engagement evidence should be interpreted according to the source paper’s reported method and sample.
• However, teachers often lack the AI literacy necessary to navigate these tools, which can lead to the perpetuation of misconceptions and biases in the classroom.

• However, teachers often lack the AI literacy necessary to navigate these tools, which can lead to the perpetuation of misconceptions and biases in the classroom.

Understanding K-12 Teachers’ Needs for AI Education: A Survey-Based Study Nazan Bautista, John Femiani, Daniela Inclezan Miami University Oxford, OH 45056, USA nubautista@miamioh.edu, femianjc@miamioh.edu, inclezd@miamioh.edu Abstract With the rapid rise of AI technologies such as ChatGPT, un- derstanding and integrating AI into K-12 education has be- come increasingly important. However, teachers often lack the AI literacy necessary to navigate these tools, which can lead to the perpetuation of misconceptions and biases in the classroom.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.
• Require human review of generated outputs and explicit guidance against over-reliance or answer copying.
• Include bias, fairness, transparency, and social impact discussion as part of the learning design.

• Survey
• Activity documentation

• Can be used as an AAB evidence record for cross-case comparison, standards drafting, and evidence-maturity mapping.
• Supports identification of recurring patterns in AI literacy, AI education implementation, teacher preparation, assessment, and responsible AI learning.

• AI literacy
• Conceptual understanding
• Ethics and responsible use
• Teacher readiness
• Curriculum / course design
• Teacher professional development
• Ethics / responsible AI education
• LLM/Chat

• Completed

K-12, Higher education

In-school (K-12), Higher education

LLM/Chat

Hands-on / experiential learning

Medium

Medium

Understanding K-12 Teachers’ Needs for AI Education: A Survey-Based Study

• Nazan Bautista
• John Femiani
• Daniela Inclezan

Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 39 No. 28, EAAI-25

2025

10.1609/aaai.v39i28.35183

https://ojs.aaai.org/index.php/AAAI/article/view/35183

https://ojs.aaai.org/index.php/AAAI/article/view/35183/37338

020_Understanding K-12 Teachers#U2019 Needs for AI Education_ A Survey-Based Study.pdf

8

Understanding K-12 Teachers’ Needs for AI Education: A Survey-Based Study Nazan Bautista, John Femiani, Daniela Inclezan Miami University Oxford, OH 45056, USA nubautista@miamioh.edu, femianjc@miamioh.edu, inclezd@miamioh.edu Abstract With the rapid rise of AI technologies such as ChatGPT, un- derstanding and integrating AI into K-12 education has be- come increasingly important. However, teachers often lack the AI literacy necessary to navigate these tools, which can lead to the perpetuation of misconceptions and biases in the classroom. This study seeks to identify K-12 teachers’ self- identified needs regarding AI education and compare them with existing research on professional development (PD) for AI integration. We surveyed 34 K-12 teachers to assess their knowledge of AI, identify areas where they require further support, and evaluate the relevance of current PD offerings. Our findings reveal a significant disconnect between the top- down assumptions of expert-driven PD initiatives and the practical needs articulated by teachers. Key themes emerged, including a diverse range of AI understanding among edu- cators, a strong preference for hands-on, practical training, and a demand for ongoing institutional support. Additionally, teachers expressed a desire for collaborative learning environ- ments to share strategies and experiences related to AI. This study underscores the importance of tailoring PD programs to address the unique contexts and challenges faced by educa- tors, advocating for a more personalized approach that fosters confidence and competence in AI integration. By aligning PD offerings with teachers’ needs, we aim to enhance their abil- ity to effectively utilize AI tools in the classroom, ultimately enriching the educational experience for students.

• In-school (K-12)
• Higher education

• Teacher readiness, time, support, and classroom integration may affect implementation quality.
• AI output reliability, hallucination, academic integrity, and age-appropriate use require safeguards.
• Use with minors requires attention to privacy, consent, data minimization, and adult supervision.

Not specified in extracted text unless noted in duration field.

Requires educators/researchers/facilitators with sufficient AI literacy and pedagogy knowledge for the target learners.

Infrastructure depends on AI tool type, learner devices, data access, and institutional policy context.

High

• duration

This entry was automatically extracted from the PDF text and manifest metadata. Fields should be manually verified before public registry publication, especially group size, location, duration, and outcome claims.