Faculty Publications and Conference Presentations

<p>Infertility, particularly among individuals undergoing in-vitro fertilization (IVF), is commonly accosiated with substantial psychological stress. This study examines interactions in a Chinese online support group (OSG) focused on IVF-related discussions, with particular attention to how textual and emotional features reflect variations across treatment stages and user roles (posters vs. commenters). Using the Chinese Linguistic Inquiry and Word Count Dictionary (CLIWC) 2015 and statistical analysis, we identified 25 significant markers from a dataset of 1.64 million Chinese characters. Notably, personal pronouns in frequency peaked during treatment, with informal language and family-related terms more prevalent before and after. Emotionally, expressions shifted from negative pre-treatment to more positive in later stages. In role-specific practices, posters mainly engage in self-disclosure and help-seeking during treatment, whereas commenters provide empathy and validation in the initial stage. These findings highlight the co-construction of support and identity in Chinese infertility discourse, revealing how online communities facilitate emotional coping and social connectedness throughout IVF treatment cycles.</p>

<p>With the rise of Generative AI technology, numerous studies have compared linguistic features of human-produced and AI-generated writings. However, little attention has been paid to the use of phrasal verbs (PVs), a difficult type of two-part verbs, in academic writing. This study chose the 1.7-million-word arts and humanities essays from the British Academic Written English corpus and generated an equivalent AI-written corpus using ChatGPT3.5. Extracting PVs with an innovative dependency-based method, the authors compared the frequency, diversity, and disciplinary distribution of all the identified PVs, and examined the polysemy, semantic transparency, and collocations of the two PVs with the highest frequency in both corpora. Findings show that human writers used PVs approximately five times more than ChatGPT3.5 and demonstrated twice the PV diversity of ChatGPT3.5. There were also significant sub-disciplinary differences in both corpora, with essays in archaeology and linguistics using significantly fewer PVs than classics and comparative American studies. Regarding PV-specific comparison, humans used PVs with more meanings, more varied transparency, and broader collocations than ChatGPT3.5. Overall, this study revealed that human writing is more flexible, personal, and spontaneous, while AI writing is more formulaic, rigid, and predictable, which provides important implications for education, applied linguistics, and computer science.</p>

<p>The present study investigates whether congruency facilitation and figurative interference—two counteractive effects—persist in L2 collocational processing when both congruency and figurativeness are present. A primed lexical decision task was administered to 44 L1-Chinese L2-English learners and 40 L1-English speakers to assess response times for figurative congruent collocations, along with their matched literal congruent and figurative incongruent collocations. Results showed that while collocational priming was absent, both congruency facilitation and figurative interference emerged, with their effects modulated by L2 proficiency. Specifically, in low-proficiency learners, congruency facilitation appeared to outweigh figurative interference, whereas in high-proficiency learners, figurative interference became more pronounced as L1-based facilitation was suppressed. These findings suggest that L2 learners initially rely on their activated L1 semantic network but gradually shift toward developing L2 collocational representations as proficiency increases, though these representations may remain weak and insufficient to facilitate collocate access.</p>

<p>Through case studies of the translanguaging performances of three Latinx bilingual students in U.S. classrooms, as well as an examination of the languaging actions of Hongkongers involved in virtual and public life during a period of social change and protest, the chapter demonstrates how speakers transgress language constraints imposed by institutions and nation-states by expanding or suppressing elements in their repertoire to signify and speak. The chapter shows how speakers' translanguaging creates networks of signification for what had been rendered as indecible/unspeakable by the logic of a colonial, global capitalist society.</p>

<Abstract of the presentation>

  In today's world, where IT and AI utilization are advancing, educational activities are transforming, and the creation of teaching materials has significantly evolved. There is a growing demand for materials and tools that allow learners to study freely, anytime and anywhere. Canva is an online tool that enables anyone to create professional-looking designs using a rich variety of resources. It allows for the condensation of teaching materials, making short, focused learning possible. Additionally, as an e-learning tool, it provides the flexibility to study anytime, anywhere, making it suitable for students to adapt to the changing times.

 ＜発表要旨＞

　Canvaを使っての教材作成－教材の作成とWell-being

　IT 化、AI 活用の進む今日、教育活動は変容し、教材作成も大きく変わってきた。いつでもどこでも自由に学習ができる教材やToolが求められる。　Canvaは誰でも、豊富な素材を活用してプロのようなデザインが作成できるOnline Tool である。教材を凝縮でき、短時間集中学習が可能である。また、e-Learningとして、いつでもどこでも学習が可能である。学生は、時代の流れに応じた学習が可能である。　教師は自分らしい教材が創れ、Teaching ＆ Learningの新しい姿を同僚とも分かち合えると考える。

＜本発表についての概略＞　

Zoomによるオンライン・ライブ開催
開催日時：2026年3月21日（土）21:00～23:00（日本時間）
言語　　：日本語
参加資格：世界中の日本語教育・学習者
参加費　：無料
申込締切：2026年3月13日（金）23:59（日本時間）

Web page：https://kokusaionline.wixsite.com/kouryukai2026

Programme

https://kokusaionline.wixsite.com/kouryukai2026/schedulehttps://kokusaionline.wixsite.com/kouryukai2026/schedule

A Comparative Analysis of AI Grading Tools: Efficiency, Pedagogy, and the Human-in-the-Loop <br/><br/>Abstract <br/>The integration of Artificial Intelligence (AI) into educational assessment promises to alleviate teacher workload and accelerate feedback cycles. This study conducts a comparative analysis of three prominent AI grading tools—Gradescope, CoGrader, and Pregrade—evaluating their technical approaches, pedagogical alignment, and impact on teacher agency. Through a framework examining automation level, feedback quality, and integration design, the analysis reveals distinct models: Gradescope’s AI-assisted answer grouping for scalable efficiency, CoGrader’s rubric-based essay analysis for formative feedback, and Pregrade’s automated scoring. Findings from literature and tool documentation indicate that while all tools offer significant time savings, their effectiveness and acceptance are contingent on preserving meaningful human oversight. The discussion argues that the most sustainable implementation follows a “human-in-the-loop” model, where AI handles initial processing and pattern recognition, empowering teachers to provide the nuanced, contextual feedback essential for deep learning. The paper concludes with implications for ethical tool selection and professional development to ensure these technologies augment, rather than replace, pedagogical expertise. <br/><br/>Keywords: automated grading, AI in education, formative feedback, teacher workload, comparative analysis <br/><br/>1. Introduction <br/><br/>The administrative burden of grading represents a significant and persistent challenge in education, consuming time that educators could otherwise devote to instruction, curriculum development, and direct student support (Tian et al., 2025). The integration of Artificial Intelligence (AI) into educational technology offers a potential solution, with a new generation of tools promising to streamline assessment workflows. These platforms claim not only to reduce grading time, sometimes by up to 80%, but also to provide more consistent and immediate feedback, a factor critically linked to student learning outcomes (Hattie &amp; Timperley, 2007). <br/><br/>However, the adoption of AI grading is not a simple matter of efficiency. It raises fundamental pedagogical questions about the nature of assessment, the role of feedback, and the preservation of teacher professionalism. Research indicates that while teachers value AI’s capacity for rapid feedback, they often distrust fully automated scoring and emphasize the necessity of human oversight (Selvam &amp; Vallejo, 2025). Furthermore, the effectiveness of these tools varies greatly depending on their design philosophy, from those that automate simple scoring to those that aim to support complex formative assessment. <br/><br/>This paper addresses this complex landscape by presenting a structured comparative analysis of three AI grading tools: Gradescope, CoGrader, and Pregrade. The objective is to move beyond marketing claims and critically examine how these tools’ functionalities align with pedagogical goals. The analysis is guided by a framework evaluating each tool’s (1) approach to automation and human oversight, (2) quality and pedagogical design of feedback, and (3) integration into existing teaching workflows. This work contributes to the ongoing conference dialogue by providing educators, administrators, and developers with evidence-based insights for selecting and implementing AI grading tools that truly enhance, rather than undermine, effective teaching and learning. <br/><br/>2. Methodology <br/><br/>This study employs a comparative case study methodology, analyzing three AI grading tools as distinct cases within the broader phenomenon of automated assessment. The primary units of analysis are the platforms’ designed functionalities, features, and stated pedagogical alignments as presented in their official documentation, published reviews, and related academic literature. <br/><br/>The three tools were selected to represent a spectrum of approaches within the AI grading landscape: <br/><br/>Gradescope: Selected for its widespread institutional adoption and unique AI-assisted answer grouping model. <br/><br/>CoGrader: Chosen for its explicit focus on rubric-based essay grading and formative feedback generation. <br/><br/>Pregrade: Included as a representative of automated scoring tools, providing a contrast in the level of proposed automation. <br/><br/>Data was gathered from official product websites and help guides as well as independent educator reviews and tool comparisons. This triangulation of sources helps mitigate vendor bias and grounds the analysis in both practical application and theoretical concern. <br/><br/>The analysis is structured using a consistent framework applied to each tool, focusing on three core dimensions derived from key themes in the literature: <br/><br/>Automation Model &amp; Teacher Agency: How does the tool integrate AI? Does it position the teacher as a final reviewer or delegate scoring authority? <br/><br/>Feedback Philosophy &amp; Pedagogical Alignment: What type of feedback does the tool generate (e.g., numeric score, grouped comments, personalized narrative)? Is it designed for efficiency, formative growth, or both? <br/><br/>Workflow Integration &amp; Data Use: How does the tool connect with existing Learning Management Systems (LMS)? What claims are made about data privacy and the use of student work for AI training? <br/><br/>This qualitative analysis aims to synthesize patterns, contrasts, and implications, providing a nuanced understanding of how different technological designs embody different assumptions about teaching and learning. <br/><br/>3. Results and Discussion <br/><br/>The comparative analysis reveals three distinct paradigms for AI integration in grading, each with specific strengths, limitations, and pedagogical implications. <br/><br/>3.1 Tool Comparison: Paradigms of AI Assistance <br/><br/>Table 1: Comparative Analysis of AI Grading Tools <br/><br/>Feature / Tool <br/><br/>Gradescope <br/><br/>CoGrader <br/><br/>Pregrade <br/><br/>Core AI Model <br/><br/>AI-assisted answer grouping for batch grading. <br/><br/>Rubric-based analysis for automated essay scoring &amp; feedback. <br/><br/>Automated scoring and feedback generation. <br/><br/>Primary Use Case <br/><br/>Scaling grading for large classes; diverse formats (bubble sheets, handwritten math, code). <br/><br/>Providing detailed, formative feedback on written assignments and essays. <br/><br/>Automated assessment of student responses. <br/><br/>Teacher's Role <br/><br/>Manager &amp; Reviewer: Manages AI-grouped answers, grades by group, provides overarching group feedback. <br/><br/>Editor &amp; Final Authority: Reviews, adjusts, and approves all AI-generated scores and comments before release. <br/><br/>Overseer: Relies on automated output, with presumably limited intervention. <br/><br/>Feedback Type <br/><br/>Efficient, consistent application of rubric points; group-level comments. <br/><br/>Detailed, criterion-based "glows and grows" narrative feedback tailored to a rubric. <br/><br/>Automated scores and comments. <br/><br/>Key Strength <br/><br/>Unmatched efficiency for objective or semi-objective questions in high-volume settings. <br/><br/>High-quality, timely formative feedback that teachers can personalize, supporting writing development. <br/><br/>High degree of automation for standardized responses. <br/><br/>Key Limitation <br/><br/>Less effective for highly unique, open-ended responses; feedback is less personalized. <br/><br/>Primarily focused on text-based essays and written responses. <br/><br/>Potential misalignment with nuanced pedagogical goals; risks bypassing teacher judgment. <br/><br/>3.2 Discussion: Balancing Efficiency, Pedagogy, and Trust <br/><br/>The findings highlight a central tension in AI grading: the trade-off between scalable efficiency and pedagogically meaningful, personalized feedback. Gradescope excels at the former, using AI to restructure the grading workflow itself. Its answer-grouping model is a powerful tool for consistency and speed, fundamentally changing the task from grading student-by-student to grading answer-by-answer. However, this comes at the potential cost of individualized attention, as feedback is applied to groups. <br/><br/>CoGrader, in contrast, is designed around the feedback loop. By generating specific narrative feedback against a customizable or standards-aligned rubric, it aims to provide the timely, detailed commentary that research shows is essential for learning but often logistically difficult for teachers to provide at scale. Its design philosophy explicitly keeps the teacher "in the loop" as the final authority, a feature that directly addresses educator concerns about losing agency and oversight. <br/><br/>Pregrade, representing a more fully automated model, theoretically offers the highest efficiency gain. However, academic literature suggests this model is the most problematic. Studies find that teachers deeply distrust automated scoring and that students are skeptical of feedback from AI alone. Furthermore, pedagogical research strongly advocates for separating detailed formative feedback from evaluative grades to promote a growth mindset. A tool that primarily outputs a score may inadvertently undermine this principle. <br/><br/>Therefore, the most significant differentiator among tools is not their technical sophistication, but how they architect the relationship between the teacher and the algorithm. Tools like CoGrader that adopt a "human-in-the-loop" model, where AI acts as a first-pass assistant whose work is always reviewed, align more closely with both teacher preferences and sound pedagogical practice. This model leverages AI to eliminate the drudgery of initial scoring and drafting feed...

Li and Wilson believe that effective integration of AI for language learning should ensure 1) the occurrence of learning (i.e. through social-cultural and social-constructive activities) and 2) the complete process of language learning (i.e. recursive and non-linear) (Li and Wilson, 2025). This socio-cognitive perspective of writing development informs this demonstration, which proposes AI-embedded writing instructions where AI plays purposefully-assigned roles to augment learners’ cognitive effort, writer agency, and writing skills at the tertiary level.<br/>The proposed writing instructions deploy GenAI as a passive agent, providing simulation with limited explanations. The passive GenAI can be conceptualized as a quiet TA or a co-learner who provides initial learning support. GenAI’s task modelling capacity also provides different formats of ideation and organization, allowing learners to mold and re-mold these formats according to context, audience, topic, purpose (Barnes, 2020). The customized presence of AI is used to boost cognitive effort and promote critical dialogues between teachers and students, and among students themselves, to ensure that learning does take place.

　Canvaを使っての教材作成－教材の作成とWell-being<br/><br/>　IT 化、AI 活用の進む今日、教育活動は変容し、教材作成も大きく変わってきた。いつでもどこでも自由に学習ができる教材やToolが求められる。　Canvaは誰でも、豊富な素材を活用してプロのようなデザインが作成できるOnline Tool である。教材を凝縮でき、短時間集中学習が可能である。また、e-Learningとして、いつでもどこでも学習が可能である。学生は、時代の流れに応じた学習が可能である。　教師は自分らしい教材が創れ、Teaching ＆ Learningの新しい姿を同僚とも分かち合えると考える。<br/><br/> Creating Educational Materials Using Canva – The Creation of Materials and Well-being<br/><br/> In today's world, where IT and AI utilization are advancing, educational activities are transforming, and the creation of teaching materials has significantly evolved. There is a growing demand for materials and tools that allow learners to study freely, anytime and anywhere. Canva is an online tool that enables anyone to create professional-looking designs using a rich variety of resources. It allows for the condensation of teaching materials, making short, focused learning possible. Additionally, as an e-learning tool, it provides the flexibility to study anytime, anywhere, making it suitable for students to adapt to the changing times.

The Hong Kong Education Bureau has implemented various measures to support the integration of South Asian ethnic minorities into local communities. One such measure is providing Chinese language education in primary and secondary schools, as proficiency in Chinese is considered crucial for social mobility. The measure has been implemented for a decade; positive outcomes are to be expected. However, official data about the Chinese proficiency level of South Asian ethnic minorities is limited. With the increase in the number of South Asian students from India, Pakistan, Nepal, Bangladesh, and Sri-Lanka successfully entering higher education institutions in Hong Kong, there is a need for language teachers to find out more about the current situation of this learner group and reflect on current curriculum and pedagogy. This study investigates the issues of reading acquisition of South Asian students, aged 18 to 22, and from various academic disciplines, with prior knowledge of traditional Chinese characters and Cantonese, in an English as a medium of instruction Hong Kong university in their Chinese language learning, specifically in intermediate Chinese reading and writing courses. Preliminary results show that the South Asian students acknowledge the advantage of their prior knowledge, that is, understanding of radicals and the full handwriting ability of a traditional characters, while having some difficulties in recognising simplified characters such as characters which are totally different in form. Practitioners can enhance their awareness and understanding of students’ learning backgrounds and difficulties to facilitate their learning. Additionally, relevant learning materials, such as comparisons of traditional and simplified radicals and the basic rules for Chinese character conversion could be supplemented to assist students.