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Research Commons is the University of Waikato's open access research repository, housing research publications and theses produced by the University's staff and students.

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The appearance of free will in artificial intelligence: How human decision-making is influenced
(The University of Waikato, 2026) Stewart, Annabelle; Perszyk, Ken; Agar, Nick
This thesis examines how artificial intelligence (AI), in particular large language models (LLMs), influences human deliberation and the experience of free will (FW). While much of the existing literature focuses on whether AI systems genuinely possess FW or consciousness, this thesis shifts the focus to how the appearance of these affects human decision-making. It is argued that LLM AI systems show an appearance of FW (AFW) and an appearance of consciousness (AC) through observable behaviours. This appearance is enough to influence human deliberation, even when users may not believe that AI systems have genuine FW or consciousness. While AI systems do not remove alternative possibilities (APs) or take away human control, they do reshape how options are perceived and evaluated, which narrows the phenomenological experience of freedom. This influence often operates below conscious awareness, affecting reasoning processes without coercion. Traditional views of FW and moral responsibility that focus on the ability to do otherwise do not adequately account for external influence on deliberation from AI systems. The thesis concludes that responsibility and accountability should remain with humans, as AI systems function as a tool rather than morally responsible entity. However, the increasing use of AI in everyday decision-making suggests the need to reconsider how influence, human cognition, responsibility, and accountability interact in an AI-mediated environment.
Navigating researcher positionality in comparative and international education research: Perspectives from emerging researchers
(University of Sydney, 2022-11-17) Chin, M; Beckwith, Vicky; Levy, Ben; Gulati, S; Macam, AAF; Saxena, T; Suwarningsih, DPS
Articulating one’s positionality as a researcher is crucial to social research. This is particularly important in comparative and international education research where context, culture and notions of power underpin much of the work. However, researcher positionality has multiple meanings, making it challenging for emerging researchers to navigate its muddied, cluttered and unfamiliar terrain. In this article, seven emerging researchers examine their own researcher positionality within the context of their postgraduate research. Through a series of short vignettes, they draw attention to the different conceptualisations of researcher positionality and uncover the challenges and dilemmas.
De-Sisters: Understanding women’s desistance from crime in Aotearoa
(The University of Waikato, 2026) Stevens, Shannon; Curtis, Cate; Curtis, Bruce
Desistance from crime is increasingly understood as a developmental and relational process embedded within social environments. Efforts to establish a life beyond crime are frequently complicated by cumulative disadvantages, including unresolved trauma, fractured or harmful relationships, economic precarity, and enduring experiences of systemic exclusion. Despite growing scholarly interest in desistance, women’s lived experiences of navigating change remain marginal within the literature, with limited empirical attention given to the specific social, cultural, and structural contexts shaping desistance in Aotearoa New Zealand. Across the narratives of twelve women once caught in cycles of recidivism, this study offers new insight into the factors which support and obstruct lives beyond crime. Desistance is revealed as a process of self-discovery, healing and relational connection. The study maps the psychological and relational terrain of change, exposing an uneven landscape produced by minimal access to therapeutic intervention and limited cultural integration. Under these conditions, efforts to reconstruct identity were further complicated by the persistent digital trace of conviction, amplifying both private and public forms of stigma. Women leaving prison are often unprepared and underdeveloped, desisting within hazardous reintegration environments. Desistance advanced when agency was restored, highlighting the need for coordinated systemic reform to strengthen human, social, and recovery capital, modernise correctional provisions, and formalise post-release advocacy.
How positioning wearable haptic interfaces on limbs influences virtual embodiment
(IEEE, 2024) Dwivedi, Anany; Yu, Shihan; Hao, Chenxu; Salvietti, Gionata; Prattichizzo, Domenico; Beckerle, Philipp
With increasing use of computer applications and robotic devices in our everyday life, and with the advent of metaverse, there is an urgent need of developing new types of interfaces that facilitate a more intuitive interaction in physical and virtual space. In this work, we investigate the influence of the location of haptic feedback devices on embodiment of virtual hands and user load during an interactive pick-and-place task. To do this, we conducted a user study with a 3x2 repeated measure experiment design: feedback position is varied between the distal phalanx of the index finger and the thumb, the proximal phalanx of the index finger and the thumb, and the wrist. These conditions of feedback are tested with the stimuli applied synchronously to the participant in one case, and with an additional delay of 350 ms in the second case. The results show that the location of the haptic feedback device does not affect embodiment, whereas the delay, i.e., whether the feedback is applied synchronously or asynchronously, affects embodiment. This suggests that for pick-and-place tasks, haptic feedback devices can be placed on the user's wrist without compromising performance making the hands to remain free, allowing unobstructed hand visibility for precise motion tracking, thereby improving accuracy.
Leveraging large language models for automated feature extraction and model training in EMG-based motion decoding
(IEEE, 2025) Dwivedi, Anany; Guan, Bonnie; Lahr, Gustavo J. G.; Head, Mitchell A.; König, Jemma; Owen, Mahonri; Liarokapis, Minas; Bifet, Albert
Feature extraction and model training are critical steps in developing machine learning models for electromyography (EMG) based motion decoding. Traditionally, these processes require domain expertise and programming knowledge to implement signal processing algorithms with optimized model training pipelines. In this work, we investigate the feasibility of using Large Language Models (LLMs) to automate both the extraction of features from EMG data and the development of machine learning models for decoding human motion with minimal human intervention. More specifically, we compare LLM extracted features and their corresponding motion decoding models against those developed using manually developed code. Our results indicate that LLM extracted features and their corresponding trained models achieve performance comparable to traditional methods, demonstrating the potential of accelerating research and scientific investigations with AI-driven biosignal processing. This study highlights LLMs’ capabilities and limitations in replacing manual coding for developing muscle-machine interfaces and provides insights into their integration into biomedical signal analysis workflows.