arXiv subjects

Zachari Swiecki

Publications and source records attributed to Zachari Swiecki.

12 recordsLinked to original sources

arXiv2026

From Diagnosis to Redesign: Using Quantitative Ethnography to Improve Multi-Agent LLM Reasoning

Multi-agent large language model (LLM) systems are designed to improve reasoning by decomposing tasks across multiple agents with specialized functions, but the presence of multiple agents does not inherently guarantee coherent reasoning or outputs that align with task objectives. This paper introduces a quantitative ethnographic (QE) approach for diagnosing and redesigning multi-agent LLM systems based on the discourse produced through agent interactions. We test this approach using automated essay scoring as an example context, applying Epistemic Network Analysis (ENA) to model a five-agent multi-agent debate system and examine differences between debates that produced correct versus incorrect scoring decisions. Results show that, in the initial system, correct scoring decisions were characterized by rubric-grounded justification, agreement, and elaboration. Incorrect scoring decisions, in contrast, were characterized by extended proposition-challenge-response exchanges that were less consistently tied to rubric criteria. We then used the findings to revise the agents' prompts. The revised system improved exact scoring accuracy from 27.78% to 40.28% and shifted the discourse of incorrect debates toward the rubric-grounded pattern of correct ones, making the two nearly indistinguishable. Based on these results, we argue that QE can support a diagnostic-to-redesign loop for AI reasoning by tracing how patterns of agent interaction relate to system performance, informing prompt redesign, and evaluating whether those redesigns change both outcomes and interaction patterns.

cs.CL↗

arXiv2026

Dean of LLM Tutors: A Framework for Automated Quality Review of AI-generated Feedback

Large language model (LLM) tutors are increasingly used to generate educational feedback, but existing research has focused mainly on feedback generation rather than feedback evaluation. As a result, LLM-generated feedback may offer limited pedagogical value and carry risks of hallucination. The current study introduces DeanLLM, an automated review framework for comprehensively evaluating feedback generated by LLM tutors before it is shared with students. We developed a 16-dimension evaluation framework covering feedback content, educational effectiveness, and hallucination risks, and validated it using using human-expert annotations of LLM-generated tutor feedback on synthetic computer science assignment submissions derived from real coursework. We then examined whether LLMs could serve as automated LLM-generated tutor feedback reviewers, and used the best-performing reviewer to benchmark tutor feedback generated by 10 commercial LLMs. Psychometric analyses supported the reliability of the proposed framework and showed that human reviewers tended to evaluate feedback holistically, whereas the LLM reviewer separated rubric dimensions more mechanically. Standard zero-shot and few-shot prompting showed limited agreement with human experts for content-quality judgments. Supervised fine-tuning of GPT-4.1 with human-labelled examples containing scores only, without explanatory rationales, achieved the strongest alignment with expert judgments. Reasoning LLMs were particularly effective at hallucination detection and produced automated tutor feedback with stronger educational effectiveness and factuality than lightweight models. The findings indicate that DeanLLM offers a scalable way for automatically improving the reliability and safety of LLM tutor feedback, while also demonstrating that reviewer calibration and model choice remain critical for educational deployment.

cs.CY↗

arXiv2026

SCALEFeedback: A Large-Scale Dataset of Synthetic Computer Science Assignments for LLM-generated Educational Feedback Research

Using Large Language Models (LLMs) to give educational feedback to students for their assignments has attracted much attention in the AI in Education (AIED) field. Yet, there is currently no large-scale open-source dataset of student assignments that includes detailed assignment descriptions, rubrics, and student submissions across various courses. As a result, research on generalisable methodology for automatic generation of effective and responsible educational feedback remains limited. In this paper, we introduce a synthetic computer science university assignment dataset for LLM-based educational feedback research, called SCALEFeedback (Synthetic Computer science Assignments for LLM Educational Feedback Research). The dataset is generated via Sophisticated Assignment Mimicry (SAM) framework specifically designed to synthesise this dataset and that utilizes one-to-one LLM-based imitation from real assignment descriptions, rubrics, and student submissions. Our open-source dataset contains 10,000 synthetic student submissions spanning 155 assignments across 59 university-level computer science courses. Technical validation confirmed that the synthetic dataset closely resembles real data while successfully eliminating personally identifiable information present in the source material. The creation of this dataset is a valuable contribution to researchers who aim to develop LLM-based generalisable methods for offering high-quality, automated educational feedback in a scalable way.

cs.CY↗

arXiv2025

GoldMind: A Teacher-Centered Knowledge Management System for Higher Education -- Lessons from Iterative Design

Designing Knowledge Management Systems (KMSs) for higher education requires addressing complex human-technology interactions, especially where staff turnover and changing roles create ongoing challenges for reusing knowledge. While advances in process mining and Generative AI enable new ways of designing features to support knowledge management, existing KMSs often overlook the realities of educators' workflows, leading to low adoption and limited impact. This paper presents findings from a two-year human-centred design study with 108 higher education teachers, focused on the iterative co-design and evaluation of GoldMind, a KMS supporting in-the-flow knowledge management during digital teaching tasks. Through three design-evaluation cycles, we examined how teachers interacted with the system and how their feedback informed successive refinements. Insights are synthesised across three themes: (1) Technology Lessons from user interaction data, (2) Design Considerations shaped by co-design and usability testing, and (3) Human Factors, including cognitive load and knowledge behaviours, analysed using Epistemic Network Analysis.

cs.HC↗

arXiv2025

Towards Reliable Generative AI-Driven Scaffolding: Reducing Hallucinations and Enhancing Quality in Self-Regulated Learning Support

Generative Artificial Intelligence (GenAI) holds a potential to advance existing educational technologies with capabilities to automatically generate personalised scaffolds that support students' self-regulated learning (SRL). While advancements in large language models (LLMs) promise improvements in the adaptability and quality of educational technologies for SRL, there remain concerns about the hallucinations in content generated by LLMs, which can compromise both the learning experience and ethical standards. To address these challenges, we proposed GenAI-enabled approaches for evaluating personalised SRL scaffolds before they are presented to students, aiming for reducing hallucinations and improving the overall quality of LLM-generated personalised scaffolds. Specifically, two approaches are investigated. The first approach involved developing a multi-agent system approach for reliability evaluation to assess the extent to which LLM-generated scaffolds accurately target relevant SRL processes. The second approach utilised the "LLM-as-a-Judge" technique for quality evaluation that evaluates LLM-generated scaffolds for their helpfulness in supporting students. We constructed evaluation datasets, and compared our results with single-agent LLM systems and machine learning approach baselines. Our findings indicate that the reliability evaluation approach is highly effective and outperforms the baselines, showing almost perfect alignment with human experts' evaluations. Moreover, both proposed evaluation approaches can be harnessed to effectively reduce hallucinations. Additionally, we identified and discussed bias limitations of the "LLM-as-a-Judge" technique in evaluating LLM-generated scaffolds. We suggest incorporating these approaches into GenAI-powered personalised SRL scaffolding systems to mitigate hallucination issues and improve the overall scaffolding quality.

cs.CY↗

arXiv2025

Capturing and Sharing Know-How through Visual Process Representations: A Human-Centred Approach to Teacher Workflows

Knowledge Management is crucial for capturing and transferring expertise within universities, especially in high staff turnover contexts where expertise loss disrupts teaching. Documenting teachers' workflows is time-intensive and diverts experts from core responsibilities. Sequential Pattern Mining (SPM) leverages log data to identify expert workflows, offering an automated alternative to represent workflows but requiring transformation into intuitive formats for novice educators. This paper introduces Visual Process Representations (VPR), a design approach combining SPM, Knowledge Management processes, and storytelling techniques to convert expert log data into clear visualisations. We detail the design phases and report a study evaluating visual affordances (text lists vs. pictorial-style) and teachers' perceptions of four versions of the VPR with 160 higher teachers on Prolific. Results indicate improved task performance, usability, and engagement, particularly with enriched visuals, though process memorability and task time improvements were limited. The findings highlight VPR's potential to visualise workflows and support novice educators.

cs.HC↗

arXiv2024

Self-regulated Learning Processes in Secondary Education: A Network Analysis of Trace-based Measures

While the capacity to self-regulate has been found to be crucial for secondary school students, prior studies often rely on self-report surveys and think-aloud protocols that present notable limitations in capturing self-regulated learning (SRL) processes. This study advances the understanding of SRL in secondary education by using trace data to examine SRL processes during multi-source writing tasks, with higher education participants included for comparison. We collected fine-grained trace data from 66 secondary school students and 59 university students working on the same writing tasks within a shared SRL-oriented learning environment. The data were labelled using Bannert's validated SRL coding scheme to reflect specific SRL processes, and we examined the relationship between these processes, essay performance, and educational levels. Using epistemic network analysis (ENA) to model and visualise the interconnected SRL processes in Bannert's coding scheme, we found that: (a) secondary school students predominantly engaged in three SRL processes -- Orientation, Re-reading, and Elaboration/Organisation; (b) high-performing secondary students engaged more in Re-reading, while low-performing students showed more Orientation process; and (c) higher education students exhibited more diverse SRL processes such as Monitoring and Evaluation than their secondary education counterparts, who heavily relied on following task instructions and rubrics to guide their writing. These findings highlight the necessity of designing scaffolding tools and developing teacher training programs to enhance awareness and development of SRL skills for secondary school learners.

cs.HC↗

arXiv2024

Ink and Algorithm: Exploring Temporal Dynamics in Human-AI Collaborative Writing

The advent of Generative Artificial Intelligence (GAI) has revolutionized the field of writing, marking a shift towards human-AI collaborative writing in education. However, the dynamics of human-AI interaction in the collaborative writing process are not well understood, and thus it remains largely unknown how human learning can be effectively supported with such cutting-edge GAI technologies. In this study, we aim to bridge this gap by investigating how humans employ GAI in collaborative writing and examining the interplay between the patterns of GAI usage and human writing behaviors. Considering the potential varying degrees to which people rely on GAI usage, we proposed to use Dynamic Time Warping time-series clustering for the identification and analysis of common temporal patterns in AI usage during the human-AI collaborative writing processes. Additionally, we incorporated Epistemic Network Analysis to reveal the correlation between GAI usage and human writing behaviors that reflect cognitive processes (i.e., knowledge telling, knowledge transformation, and cognitive presence), aiming to offer insights for developing better approaches and tools to support human to learn effectively via such human-AI collaborative writing activities. Our findings reveal four major distinct temporal patterns in AI utilization and highlight significant correlations between these patterns and human writing behaviors. These findings have significant implications for effectively supporting human learning with GAI in educational writing tasks.

cs.HC↗

arXiv2024

Putting Our Minds Together: Iterative Exploration for Collaborative Mind Mapping

We delineate the development of a mind-mapping system designed concurrently for both VR and desktop platforms. Employing an iterative methodology with groups of users, we systematically examined and improved various facets of our system, including interactions, communication mechanisms and gamification elements, to streamline the mind-mapping process while augmenting situational awareness and promoting active engagement among collaborators. We also report our observational findings on these facets from this iterative design process.

cs.HC↗

arXiv2024

Evidence-centered Assessment for Writing with Generative AI

We propose a learning analytics-based methodology for assessing the collaborative writing of humans and generative artificial intelligence. Framed by the evidence-centered design, we used elements of knowledge-telling, knowledge transformation, and cognitive presence to identify assessment claims; we used data collected from the CoAuthor writing tool as potential evidence for these claims; and we used epistemic network analysis to make inferences from the data about the claims. Our findings revealed significant differences in the writing processes of different groups of CoAuthor users, suggesting that our method is a plausible approach to assessing human-AI collaborative writing.

cs.HC↗

arXiv2023

Human-Centred Learning Analytics and AI in Education: a Systematic Literature Review

The rapid expansion of Learning Analytics (LA) and Artificial Intelligence in Education (AIED) offers new scalable, data-intensive systems but also raises concerns about data privacy and agency. Excluding stakeholders -- like students and teachers -- from the design process can potentially lead to mistrust and inadequately aligned tools. Despite a shift towards human-centred design in recent LA and AIED research, there remain gaps in our understanding of the importance of human control, safety, reliability, and trustworthiness in the design and implementation of these systems. We conducted a systematic literature review to explore these concerns and gaps. We analysed 108 papers to provide insights about i) the current state of human-centred LA/AIED research; ii) the extent to which educational stakeholders have contributed to the design process of human-centred LA/AIED systems; iii) the current balance between human control and computer automation of such systems; and iv) the extent to which safety, reliability and trustworthiness have been considered in the literature. Results indicate some consideration of human control in LA/AIED system design, but limited end-user involvement in actual design. Based on these findings, we recommend: 1) carefully balancing stakeholders' involvement in designing and deploying LA/AIED systems throughout all design phases, 2) actively involving target end-users, especially students, to delineate the balance between human control and automation, and 3) exploring safety, reliability, and trustworthiness as principles in future human-centred LA/AIED systems.

cs.CY↗

arXiv2023

Human-AI Collaboration in Thematic Analysis using ChatGPT: A User Study and Design Recommendations

Generative artificial intelligence (GenAI) offers promising potential for advancing human-AI collaboration in qualitative research. However, existing works focused on conventional machine-learning and pattern-based AI systems, and little is known about how researchers interact with GenAI in qualitative research. This work delves into researchers' perceptions of their collaboration with GenAI, specifically ChatGPT. Through a user study involving ten qualitative researchers, we found ChatGPT to be a valuable collaborator for thematic analysis, enhancing coding efficiency, aiding initial data exploration, offering granular quantitative insights, and assisting comprehension for non-native speakers and non-experts. Yet, concerns about its trustworthiness and accuracy, reliability and consistency, limited contextual understanding, and broader acceptance within the research community persist. We contribute five actionable design recommendations to foster effective human-AI collaboration. These include incorporating transparent explanatory mechanisms, enhancing interface and integration capabilities, prioritising contextual understanding and customisation, embedding human-AI feedback loops and iterative functionality, and strengthening trust through validation mechanisms.

cs.HC↗