Teo Sušnjak

Biographic Data

ID8243383
NAMETeo Sušnjak
GIVEN NAMESTeo
FAMILY NAMESušnjak
SIGNATURESUŠNJAK T
AFFILIATIONSMassey University
ORCID0000-0001-9416-1435
VERIFIEDYes
TOTAL WORKS5
TOTAL CITATIONS0
WORKS AS AUTHOR5
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2020
LATEST PUBLICATION YEAR2026
H-INDEX0

  • A design science blueprint for an orchestrated AI assistant in doctoral supervision

    Open AccessTeo Sušnjak, Timothy R McIntosh et al.ARTICLETechnology, Pedagogy and Education2026

    This study presents a design science blueprint for an orchestrated AI assistant in doctoral supervision. It synthesises recent supervision gaps and maps them to the capabilities of an orchestrated assistant built around large language models (LLMs) through a severity–mitigability triage, grounded in Stakeholder Theory and bounded by Academic Integrity. The proposed artefact addresses core limitations of LLMs through agentic workflows that extend …

  • Large language models for scientometric mapping of scientific controversy

    Open AccessTeo Sušnjak, Cole Palffy et al.ARTICLEScientometrics2026

    This study advanced large language model (LLM)-enabled scientometric methods for analysing contested scholarly communication through a hybrid AI–human workflow for large-scale mapping of stance and themes in scientific corpora. We treated the literature as a large textual record and examined how competing knowledge claims were distributed across venues and over time. We used the Lyme disease controversy as a testbed, applied multiple LLMs to thou…

  • ChatGPT

    Open AccessTeo Sušnjak, Timothy R McIntoshARTICLEEducation Sciences2024

    This study addresses the significant challenge posed by the use of Large Language Models (LLMs) such as ChatGPT on the integrity of online examinations, focusing on how these models can undermine academic honesty by demonstrating their latent and advanced reasoning capabilities. An iterative self-reflective strategy was developed for invoking critical thinking and higher-order reasoning in LLMs when responding to complex multimodal exam questions…

  • Current stance on predictive analytics in higher education

    Rahila Umer, Teo Sušnjak et al.ARTICLEInteractive Learning Environments2023

    Predictive models on students’ academic performance can be built by using historical data for modelling students’ learning behaviour. Such models can be employed in educational settings to determine how new students will perform and in predicting whether these students should be classed as at-risk of failing a course. Stakeholders can use predictive models to detect learning difficulties faced by students and thereby plan effective interventions …

  • A systematic literature review

    Open AccessVajisha Udayangi Wanniarachchi, Anuradha Mathrani et al.ARTICLEInternational Journal of…2020