title: Motivation factors for students using Generative AI

authors: Sheridan, Brendan Daniel

subjects: Academic Integrity

abstract: Generative Artificial Intelligence (GenAI) has caused a shift in approaches to assessment and academic integrity in tertiary institutions. Recent research internationally and within Australasia underscores the need for responsible GenAI conduct and preparation for a future where work and education are shaped by GenAI technologies. However, ethical considerations often need to be made based on the impact on human research, intellectual property rights, and AI literacy in higher education. Academic integrity and ethical considerations should be used to balance hasty approaches to GenAI to ensure tertiary institutions provide inclusive learning opportunities. All of these impact on the need to understand what motivates student use of GenAI. Using the framework proposed by Bouteraa et al. (2024) as a model, this presentation applies data and results from recent literature to explore the factors which motivate use of Generative AI in a tertiary education context. These factors consist of Performance Expectancy, Effort Expectancy, Technological Self-Efficacy, Educational Self-Efficacy, Integrity, and Personal Anxiety. The presentation highlights what factors make students likely to use Generative AI, what factors demotivate use, and how motivation and demotivation do not always produce a desired outcome.

text: Motivation factors for students using Generative AI Generative Artificial Intelligence (GenAI) has caused a shift in approaches to assessment and academic integrity in tertiary institutions. Recent research internationally and within Australasia underscores the need for responsible GenAI conduct and preparation for a future where work and education are shaped by GenAI technologies. However, ethical considerations often need to be made based on the impact on human research, intellectual property rights, and AI literacy in higher education. Academic integrity and ethical considerations should be used to balance hasty approaches to GenAI to ensure tertiary institutions provide inclusive learning opportunities. All of these impact on the need to understand what motivates student use of GenAI. Using the framework proposed by Bouteraa et al. (2024) as a model, this presentation applies data and results from recent literature to explore the factors which motivate use of Generative AI in a tertiary education context. These factors consist of Performance Expectancy, Effort Expectancy, Technological Self-Efficacy, Educational Self-Efficacy, Integrity, and Personal Anxiety. The presentation highlights what factors make students likely to use Generative AI, what factors demotivate use, and how motivation and demotivation do not always produce a desired outcome.

year: 2025-09-05

title: Measuring electrical properties of batteries at ultra-long timescales

authors: Wilson, Marcus T.

abstract: Quantifying battery behavior is critical to development of new battery technologies and energy storage systems. While it is straightforward to measure properties such as impedance at short timescales (i.e. frequencies larger than ~1 Hz) the relevance of this is questionable since rechargeable batteries in normal usage are often cycled on timescales of hours or days. Making measurements at these timescales, for example impedance measurements below ~1 mHz, is more challenging. In this paper we discuss approaches to quantifying battery behavior at timescales from hours to weeks (frequency scales of ~0.1 mHz down to ~1 μHz). We present frequency domain measurements and time domain measurements, achieved through four-point measurements with a Keysight 66332A at around 100 mA RMS. At low frequencies significant charge is shifted in a measurement cycle, complicating interpretation. The digitization of a sine-wave may introduce errors such as constant current offsets that build in significance with time. The operating point (DC voltage level) of the battery should be controlled since it influences impedance at the lowest frequencies as a result of the voltage-dependent nature of different electrochemical processes. We relate the voltage-dependent effects to time-domain measurements such as cyclic voltammetry and incremental capacity analysis.

text: Measuring electrical properties of batteries at ultra-long timescales Quantifying battery behavior is critical to development of new battery technologies and energy storage systems. While it is straightforward to measure properties such as impedance at short timescales (i.e. frequencies larger than ~1 Hz) the relevance of this is questionable since rechargeable batteries in normal usage are often cycled on timescales of hours or days. Making measurements at these timescales, for example impedance measurements below ~1 mHz, is more challenging. In this paper we discuss approaches to quantifying battery behavior at timescales from hours to weeks (frequency scales of ~0.1 mHz down to ~1 μHz). We present frequency domain measurements and time domain measurements, achieved through four-point measurements with a Keysight 66332A at around 100 mA RMS. At low frequencies significant charge is shifted in a measurement cycle, complicating interpretation. The digitization of a sine-wave may introduce errors such as constant current offsets that build in significance with time. The operating point (DC voltage level) of the battery should be controlled since it influences impedance at the lowest frequencies as a result of the voltage-dependent nature of different electrochemical processes. We relate the voltage-dependent effects to time-domain measurements such as cyclic voltammetry and incremental capacity analysis.

year: 2023

title: He Tangata Kei Tua: Guidelines for biobanking with Māori.

authors: Hudson, Maui

abstract: Māori ethical frameworks recognise that all research in New Zealand is of interest to Māori and outline community expectations of appropriate behavior in research to deliver the best outcomes for Māori. Research contributes to the broader development objectives of society and this endeavor is being supported by biobanking infrastructure. Ethics has a specific role in guiding key behaviours, processes and methodologies used in research.
This document outlines a framework for addressing Māori ethical issues within the context of biobanking. It draws on a foundation of mātauranga (Indigenous knowledge) and tikanga Māori (Māori protocols and practices) and will be useful for researchers, ethics committee members and those who engage in consultation or advice about biobanking with Māori in local, regional, national or international settings.

text: He Tangata Kei Tua: Guidelines for biobanking with Māori. Māori ethical frameworks recognise that all research in New Zealand is of interest to Māori and outline community expectations of appropriate behavior in research to deliver the best outcomes for Māori. Research contributes to the broader development objectives of society and this endeavor is being supported by biobanking infrastructure. Ethics has a specific role in guiding key behaviours, processes and methodologies used in research.
This document outlines a framework for addressing Māori ethical issues within the context of biobanking. It draws on a foundation of mātauranga (Indigenous knowledge) and tikanga Māori (Māori protocols and practices) and will be useful for researchers, ethics committee members and those who engage in consultation or advice about biobanking with Māori in local, regional, national or international settings.

year: 2016