Attitudes Toward AI as Communicative Partners: A Human–Machine Communication (HMC) Analysis of Undergraduate Language Learners
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Abstract
Language learning has been revolutionized by the incorporation of AI-mediated language communication systems (AIMLCS), which provide individualized, scalable assistance. Nevertheless, little is known about how this particular group of undergraduate language learners views AI as collaborative partners as opposed to merely tools. This study examines undergraduate language learners' perceptions of AIMLCS as relational entities using a Human-Machine Communication (HMC) lens. Four HMC-derived dimensions: Functional Value, Relational Comparison, Partnership Perception, and Attitudinal Growth, were used in an exploratory quantitative survey of 55 Malaysian university students to gauge attitudes. Descriptive statistics were used to describe learner attitudes across the specified dimensions systematically. Specifically, mean scores and standard deviations were calculated. The findings demonstrate a dualistic viewpoint: students frequently identify a disparity in affective response, pointing out that these tools provide less emotional depth than human teachers, yet greatly appreciating the functional benefits of AIMLCS. AI is not seen by learners as a replacement for human connection, but rather as a complementary partner inside the learning environment. Additionally, respondents who reported continuous use also expressed more favourable opinions; however, longer-term studies are required to verify real changes in attitudes. The findings highlight the necessity of creating AI systems that are both communicatively and instructionally successful. By linking HMC theory and language pedagogy, this study offers educators and developers preliminary exploratory insights to optimize AI integration. It addresses both functional capabilities and relational restrictions to promote more supportive and engaging language-learning experiences.
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