A Corpus-Based Study of Evaluative Adjectives in Technology Discourse Grounded in Appraisal Theory for EFL Lexical Awareness

This paper analyzes evaluative adjectives in technological discourse, based on data from the Corpus of Contemporary American English (COCA), to understand how technological ideas are lexically framed and how these patterns can be used to inform English as a Foreign Language (EFL) lexical awareness. Eight technology-related nouns, including AI, technology, computer, robot, metaverse, virtual reality, augmented reality, and mixed reality, were examined in terms of their frequent adjective collocates and categorized by evaluative polarity (positive and negative). Findings indicate that the majority of technology-related words receive mostly positive and neutral modifiers, including advanced, modern, and digital, which are used to describe a discourse of progress and innovation. Nevertheless, AI showed a significantly greater percentage of negative adjectives (e.g., bad, stupid, wrong), which is an indication of social ambivalence towards automation and ethics. New digital concepts, such as virtual and augmented reality, were positioned with experiential adjectives (immersive, interactive), focusing on user interaction rather than morality. These patterns can be interpreted within the framework of Appraisal Theory, as they demonstrate how evaluative adjectives encode ideology and influence in modern English. Pedagogically, the results suggest the incorporation of both corpus-based and appraisal-informed teaching methods in EFL teaching to develop evaluative competence, corpus literacy, and critical awareness of the ways language creates technological meaning.