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Amidst the surge in the popularity of augmented reality (AR), particularly in education due to the impact of the COVID-19 pandemic on remote learning, this research targets the challenge of efficient AR provider selection for educational use. The core objective revolves around introducing an encompassing decision support model, harnessing Spherical fuzzy sets (SFSs) to amplify decision-making. The study&#x2019;s initiation entails pinpointing eight pivotal criteria shaping AR provider selection via a stringent SF-Delphi procedure. The model duly factors each decision-maker&#x2019;s relative significance to ensure a fair and informed evaluation. The Spherical Weighted Arithmetic Mean (SWAM) operator amalgamates criteria weights, guaranteeing an all-encompassing and unbiased assessment. The ensuing step applies the Technique for Order of Preference by Similarity to the Ideal Solution (SF-TOPSIS) approach to systematically appraise and rank ten global AR providers. The systematic framework established in this study facilitates an impartial comparison among AR providers based on predetermined criteria. Through this research, a dual contribution is offered. Firstly, the proposed decision support model systematically and efficiently guides AR provider selection, effectively equipping decision-makers to navigate the intricacies of AR technology choices. Secondly, the research&#x2019;s impact extends to education, enriching the understanding of AR&#x2019;s applications. This knowledge fosters well-informed and innovative AR technology utilization, enhancing the overall learning experience. In conclusion, this study introduces a groundbreaking decision support model that leverages SFSs to guide the selection of educational AR providers. This structured framework empowers decision-makers and advances comprehension of AR dynamics, further driving strategic integration of AR technology in education.

A Data-Driven MCDM Approach-Based Spherical Fuzzy Sets for Evaluating Global Augmented Reality Providers in Education