Author ORCID Identifier:

https://orcid.org/0009-0004-5292-814X

Date of Graduation

12-2025

Document Type

Thesis

Degree Name

Master of Education in Curriculum and Instruction (MEd)

Degree Level

Graduate

Department

Curriculum and Instruction

Advisor/Mentor

Beck, Dennis

Committee Member

Beasley, Jennifer

Second Committee Member

Collet, Vicki

Keywords

Artificial Intelligence; Educational Technology; Equity; Ethics; Gifted Identification; Qualitative

Abstract

The present study examined teachers' perceptions of using artificial intelligence (AI) to identify gifted students. The issue is clearly relevant today; several schools continue to grapple with unfair practices and irregular assessment processes, most notably for culturally and linguistically different students. Conventional keys and expert opinion remain essential, but their limitations have been acknowledged. Newer research suggests that AI could actually improve the system by making it more efficient and able to pool data that originally surfaced biases. At the same time, issues of transparency, data privacy, and the ethics of decisions driven by AI remain unresolved. To gain an understanding of this, a total of 14 educators for K-6 in the gifted field responded to an open-ended survey about their perceptions of AI for identification technologies. The analysis revealed seven main themes: current challenges, anticipated benefits of AI, concerns and ethical dilemmas, needed supports, barriers to implementation, expected outcomes, and recommendations for leaders. Teachers perceived value in AI for efficiency and objective identification. However, they were concerned about the interpretation of results and the ethical monitoring of its use. The results show that although teachers have a positive outlook on AI, given its potential, it is also critical to provide clear guidelines and training on AI in schools so that this technology can be used responsibly. These findings provide a starting point for future research on designing ethical and fair AI-assisted methods for K-12 identification of gifted students.

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