Abstract:

The field of Academia has seen the emergence of specialized AI tools that promise to revolutionize scholarly productivity through automated literature mapping and data synthesis. In fact there is a plethora of tools that nowadays an academic can utilize. However, the successful adoption of these tools depends heavily on the trust and emotional response of the academic community. Our study suggests a comparative investigation into the Sincerity Gap between the promised tool functionality and user perception in higher education.

The research will follow a two stage methodology. The first stage includes a functional audit and comparative analysis of selected tools to categorize their core value propositions. At the second stage, our team will perform a large-scale Social Listening exercise w by analyzing user discourse within the comment sections of popular YouTube tutorials and reviews for each platform. To decode this data, the project will utilize different language models such as: SentimentR and NRC Lexicon for identifying specific markers such as trust, and RoBERTa-base and SentimentBERT (SBERT) to capture the context-heavy feedback typical of academic users.

The study aims to determine which specific AI features, such as visual mapping versus text-based summarization generate the highest levels of user satisfaction and institutional trust. It will also offer a data‑driven view of the current AI-academic landscape, highlighting the emotional factors that can lead individuals either to resist technological solutions, often referred to as Algorithm Aversion, or to embrace them with a sense of Innovation Grace. This research will offer academic institutions and educational designers a practical framework for selecting AI tools that align with their faculty and elicit students’ trust, ensuring in this way that technology is integrated ethically and effectively into the research curriculum.

Keywords:

Academic AI, User Experience (UX), Sentiment Analysis.