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Tag: Waseda University

  • Study suggests common English phrases can boost perceived fluency more than advanced vocabulary

    Language learners often assume that using rare, complex vocabulary will make their speech sound more fluent. Research suggests that there is a close relationship between formulaic expression usage in speech and acoustic features of oral fluency. This implies that using formulaic expressions leads to faster articulation speed and fewer disruptions during speech. However, in terms of how listeners perceive speakers’ fluency, the role of formulaic expressions has been unclear.

    To investigate this, Ph.D. student, Kotaro Takizawa and Research Assistant Professor Shungo Suzuki from Waseda University, Japan, analyzed speech from 102 Japanese speakers of English, each delivering an argumentative speech. They measured the use of bigram and trigram expressions (two- and three-word sequences) and had fluency judged by 10 experienced raters. The study controlled for key fluency metrics, including articulation rate, pauses, and self-corrections, to isolate the effect of formulaic expressions on fluency perception. This study was published online in the journal Studies in Second Language Acquisition on February 12, 2025.

    The findings revealed that utterance fluency (smoothness of speech delivery) was the strongest predictor of fluency perception, accounting for 61% of the variance in ratings. However, high-frequency formulaic expressions added an extra 0.8% to fluency judgments, while rarer, more complex phrases had little to no effect.

    The study also reveals that the key to sounding fluent is not about using sophisticated words; it is about using the right phrases. Their study shows that common, everyday expressions have a small but significant impact on how fluency is perceived, even when a factor like smoothness is accounted for. “We found that common, oft-used formulaic expressions, rather than rare, sophisticated ones, significantly influenced rater judgment of speakers’ fluency,” said Takizawa.

    Fluency plays a crucial role in language learning and assessment, especially in standardized tests like TOEFL and IELTS, where expert raters evaluate how natural and smooth a speaker sounds. Traditionally, fluency has been associated with speed and uninterrupted speech, but the role of formulaic expressions (common multi-word phrases) has been less clear. Previous studies suggested that these expressions help speakers communicate more smoothly, but few have examined how they influence fluency perception on their own.

    Suzuki highlighted the practical implications: “It is generally observed that language teachers and learners tend to focus more on rare words or difficult phrases that sound more proficient. However, the current findings indicate that that should not necessarily be the focus, particularly if they want to improve their fluency perceived by others.”

    This research suggests that learners should shift their focus from advanced vocabulary to mastering everyday phrases that come naturally in conversation. For example, instead of saying “I agree the idea” — which sounds unnatural — learners should use “I agree with the idea.” These common expressions are easy to find in textbooks and everyday conversations, making them more accessible for learners of all levels.

    The study has significant implications for language testing, where fluency judgments can impact scores. It suggests that test-takers should focus on integrating natural phrasal expressions into their speech while maintaining the smoothness of their speech. Highlighting the importance of both aspects, “Our research shows that there is no denying that improving fluency in utterance contributes to good fluency judgment scores,” noted Takizawa.

    This study highlights the crucial role of common expressions in shaping how fluency is perceived, offering valuable insights for language learners and educators.

  • Waseda Researchers Apply Attachment Theory to Human-AI Bonds: What EHARS Reveals About Anxiety and Avoidance

    As AI chatbots and digital assistants become part of daily life, researchers are looking beyond trust and usefulness to understand the emotional side of human-AI interaction. A team at Waseda University argues that attachment theory, long used to explain human bonds, can also help explain why some people turn to AI for comfort and guidance.

    The researchers developed a new self-report measure called the Experiences in Human-AI Relationships Scale, or EHARS, to capture how users relate to AI in ways that resemble attachment patterns. The work, based on two pilot studies and a formal study, was published in Current Psychology in May 2025.

    Measuring attachment anxiety and avoidance

    EHARS focuses on two dimensions: attachment anxiety and attachment avoidance toward AI systems. Higher anxiety is linked to seeking reassurance and worrying that an AI will respond inadequately, while higher avoidance reflects discomfort with emotional closeness and a preference for distance.

    In the study, nearly 75% of participants reported turning to AI for advice, suggesting that many people already treat AI as a source of guidance. About 39% described AI as a constant, dependable presence, a finding the authors say is relevant to how emotional security can be sought through technology.

    What the findings do and do not mean

    The authors emphasize that the results do not prove people are forming genuine human-like attachments to AI. Instead, the study indicates that established psychological frameworks may help describe patterns in human-AI relationships as these tools become more conversational and socially responsive.

    That distinction matters because AI systems can simulate empathy without experiencing it, potentially shaping user expectations and dependency. Researchers and ethicists have increasingly warned that emotionally persuasive interfaces can heighten risks for vulnerable users, particularly in loneliness and mental health contexts.

    Implications for ethical AI design

    The team suggests EHARS could help designers and researchers evaluate how different users emotionally engage with AI, informing safer interaction patterns. In practice, that could mean more transparent disclosures, careful use of relational language, and guardrails to reduce overreliance where attachment anxiety appears high.

    As AI companions, coaching bots, and therapy-adjacent apps expand, measuring emotional dynamics may become as important as testing accuracy and security. The Waseda study positions attachment-informed evaluation as one tool for aligning AI behavior with user well-being and responsible product design.