COMPARISON OF METHODS FOR SELECTING IMMERSIVE TECHNOLOGY TOOLS AND SERVICES TO SUPPORT BLENDED LEARNING IN GENERAL SECONDARY EDUCATION INSTITUTIONS

Abstract

Abstract. This article is dedicated to the pressing issue of selecting immersive technology tools and services to support blended learning in general secondary education institutions. It analyzes existing approaches to choosing such tools, considering the rapid development of technology and the growing number of available options. The introduction highlights that, despite the numerous benefits of immersive technologies (improving learning quality and material comprehension), teachers and students face the challenge of selecting appropriate services that meet their specific tasks, needs, technical readiness, and financial capabilities.
The article formulates the problem of developing methods for finding optimal services. It notes that the choice becomes more complex in the context of blended learning, where it's necessary to manage a variety of resources and accommodate individual student needs. An analysis of recent research confirms the importance of immersive technologies for modern educational innovation, particularly for inclusive education and the development of critical thinking. The paper compares four main methods for selecting services:
1. Search-based: Choosing the optimal service from an existing list based on given criteria.
2. Constructive: Formulating a description of an ideal service and then searching for one that matches the criteria.
3. Direct AI Queries: Receiving recommendations from artificial intelligence through direct queries.
4. Structured AI Queries: A sequential series of AI queries to create a specialized search table, allowing for a systematic search.
The authors detail each of these approaches, identifying common problems users face: the lack of a single list of services, the difficulty in choosing evaluation criteria, considering the needs of different users, and predicting the learning effect. As a solution, the article proposes using the structured AI queries method to create and populate a custom search database (using a Google Sheet as an example). This method allows for flexible adjustment of search criteria and the accumulation of results, making it universal and scalable for collaborative use. A key strength of the proposed approach is the ability to systematize the search and flexibly add new information, which significantly narrows the range of services that need to be analyzed manually.
A list of effective prompts and a functional service search table are provided. In the conclusions, the authors summarize that while the proposed method does not provide answers to all problematic questions, it offers an effective mechanism for solving them. Future research will focus on testing this method and organizing its collaborative use.