Improving Teaching and Learning in Higher Education through Machine Learning: Proof of Concept of AI's Ability to Assess the Use of Key Microskills

Aug 2, 2026

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Advances in artificial intelligence are creating new opportunities to strengthen teaching and learning in higher education. The study presents a proof of concept for using machine learning to assess educators’ use of four key microskills from an established international framework. Teaching videos repeatedly demonstrating these microskills were recorded to build a dataset, enabling decomposition of video components for modeling. Results indicate AI can support educators’ collaborative, reflective practice in an era of online teaching. The approach also strengthens formative assessment validity and extends use toward the full ten-microskill framework.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages17

Reading Time43 min

Introduction

Artificial intelligence in education

Research aim and question

Microskills concept and assessment rationale

education sciences Article Improving Teaching and Learning in Higher Education through Machine Learning: Proof of Concept' of AI's Ability to Assess the Use of Key Microskills Christopher Dann 1, Shirley O'Neill 1, Seyum Getenet 1, *, Subrata Chakraborty 2, Khaled Saleh 3 and Kun Yu 4 Citation: Dann, C.; O'Neill, S.; Getenet, S.; Chakraborty, S.; Saleh, K.; Yu, K. Improving Teaching and Learning in Higher Education through Machine Learning: Proof of Concept' of AI's Ability to Assess the Use of Key Microskills. Educ. Sci. 2024, 14, 886. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/educsci14080886](10.3390/educsci14080886) Academic Editors: Julie Delello and Rochell McWhorter Received: 21 May 2024 Revised: 2 August 2024 Accepted: 7 August 2024 Published: 14 August 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) . 1 School of Education, University of Southern Queensland, Springﬁeld Central, Ipswich 4300, Australia; [chris.dann@unisq.edu.au](chris.dann@unisq.edu.au) (C.D.); shirley.o'[neill@unisq.edu.au](neill@unisq.edu.au) (S.O.) 2 School of Science and Technology, University of New England, Sydney 2351, Australia; [subrata.chakraborty@une.edu.au](subrata.chakraborty@une.edu.au) 3 School of Information and Physical Sciences, The University of Newcastle, Newcastle 2300, Australia; [khaled.saleh@newcastle.edu.au](khaled.saleh@newcastle.edu.au) 4 Data Science Institute, University of Technology Sydney, Sydney 2007, Australia; [kun.yu@uts.edu.au](kun.yu@uts.edu.au) * Correspondence: [seyum.getenet@unisq.edu.au](seyum.getenet@unisq.edu.au) Abstract: Advances in artiﬁcial intelligence (AI), including intelligent machines, are opening new possibilities to support teaching and learning in higher education. This research has found a `proof of concept' in the application of machine learning in the assessment of educators' use of four key microskills, drawn from an internationally established framework. The analysis of teaching videos where these microskills were demonstrated multiple times in front of a green screen or in a space formed the data set. Multiple videos of this nature were recorded to allow for increased analysis and deconstruction of the video components to enable the application of machine learning. The results showed how AI can be used to support the collaborative and reﬂective practice of educators in a time when online teaching has become the norm. Having achieved a `proof of concept', this research has laid the groundwork to allow for the whole framework of ten microskills to be applied in this way thus adding a new dimension to its use. Providing such critical information that is not currently available in such a systematic and personalised way to educators in the higher education sector can also support the validity of formative assessment practices. Keywords: artiﬁcial intelligence; higher education; machine learning; microskills; reﬂective practice 1. Introduction The ﬁelds of teaching and learning and artiﬁcial intelligence (AI) are broad ﬁelds of knowledge, and the combination of these ﬁelds has the potential to fundamentally change the approaches and presentation of higher education programs [1] . Artiﬁcial intelligence is rapidly appearing in professional practice worldwide, and higher education and, more broadly, education is no exception. AI advances have been foregrounded as an important part of our educational future through increasing investment globally to address the continuing need for improved outcomes. AI has experienced a compound annual growth rate of nearly 48%, with global trends showing similar growth patterns [2,3] . Although r

FAQs

What is the proof of concept proposed in this study?

The study demonstrates a proof of concept for applying machine learning to assess educators’ use of four key microskills drawn from an established framework.

How was the dataset for the machine learning analysis created?

Teaching videos were recorded where the selected microskills were demonstrated multiple times, using a controlled setup such as a green screen or a formed space, to support analysis and decomposition of video components.

How can the AI output support educators’ teaching practice and assessment?

The results show AI can support educators’ collaborative and reflective practice and provide critical, systematic, and personalized information that strengthens formative assessment practices.

Aug 2, 2026

Upload by Oliver