AI Pair Programming Acceptance: A Value-Based Approach with AHP Analysis

Abstract

The emergence of Artificial Intelligence ( AI) tools is transforming every aspect of life with new opportunities and risks. An impact of AI tools can be seen in AI pair programming which is defined as a generative and creative support tool with real-time interaction. The goal of this study is to explore the AI pair programming acceptance. To identify, describe, categorize, and rank the factors affecting the acceptance of AI pairs a literature review, a research model proposal based on an extension of the Value-based Adoption Model (VAM) framework, and an Analytic Hierarchy Process (AHP) analysis is conducted. The proposed model consists of six main factors and twenty-two sub-factors which are validated with an AHP analysis including eleven experts' judgments. The findings presented the most essential factors as productivity, code accuracy, complexity, personal development, and innovativeness. The least significant factors were inspiration, motivation, intellectual property violation, AI interaction, and trust. This study provides insight to AI tool developers and producers in the context of programming on the key factors to consider for success.