AI-driven mobile application: unraveling students’ motivational feature preferences for reading comprehension

O. J. Chavez, Thelma Palaoag
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Abstract

PurposeThis study investigates user preferences for motivational features aligned with self-determination theory (SDT), emphasizing autonomy, relatedness, and competency. The study seeks to identify the most appealing and effective motivational features in AI-driven mobile apps for fostering autonomy, promoting relatedness, and enhancing feelings of competency in the context of reading comprehension.Design/methodology/approachEmploying a mixed-methods research design based on the self-determination theory, the study used triangulation of methods to enhance the research findings' quality, completeness, validity, and understanding.FindingsBoth qualitative and quantitative findings showed similarities in the emphasis on personalization, progress tracking, social interaction, gamification, differences in specific feature preferences, and nuanced recommendations, highlighting the importance of a comprehensive and user-centric development approach.Research limitations/implicationsThe study design and sample size might constrain the statistical power and generalizability of the results. The study used a convenience sample of students from one school.Practical implicationsThe synthesis of findings suggests the need for a comprehensive development strategy that incorporates both the commonalities and nuanced recommendations from qualitative and quantitative data. Combining the strengths of both findings, a holistic user-centric approach that integrates personalization, progress tracking, social interaction, and gamification is crucial for effective app development. Recognizing that individual preferences may vary, developers should consider tailoring features to accommodate a range of user needs, as indicated by qualitative and quantitative insights.Originality/valueThis comprehensive approach contributes to advancing knowledge in AI-driven mobile applications and their impact on student motivation and reading comprehension in the Philippine context. Also, it provides valuable insights for designers and developers, contributing to creating engaging and user-friendly platforms that enhance reading comprehension skills.
人工智能驱动的移动应用:解读学生阅读理解的动机特征偏好
目的 本研究调查用户对符合自我决定理论(SDT)的激励功能的偏好,强调自主性、相关性和能力。本研究旨在确定人工智能驱动的移动应用程序中最吸引人、最有效的激励功能,以在阅读理解中培养自主性、促进关联性并增强能力感。设计/方法/方法本研究采用基于自我决定理论的混合方法研究设计,使用三角测量方法来提高研究结果的质量、完整性、有效性和理解力。研究结果定性和定量研究结果都显示了在强调个性化、进度跟踪、社交互动、游戏化方面的相似性,在具体功能偏好和细致入微的推荐方面的差异,突出了以用户为中心的综合开发方法的重要性。研究局限/影响研究设计和样本量可能会限制统计能力和结果的普遍性。实际意义研究结果的综合表明,有必要制定一项全面的开发战略,将定性和定量数据中的共性和细微建议结合起来。结合这两项研究结果的优势,以用户为中心,整合个性化、进度跟踪、社交互动和游戏化的综合方法对于有效开发应用程序至关重要。认识到个人偏好可能会有所不同,开发人员应考虑根据定性和定量研究的结果,量身定制功能,以满足用户的各种需求。 原创性/价值 这种全面的方法有助于增进对人工智能驱动的移动应用程序及其对菲律宾学生学习动机和阅读理解能力的影响的了解。此外,它还为设计者和开发者提供了有价值的见解,有助于创建吸引人的、用户友好的平台,提高阅读理解能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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