Complex and rapidly evolving work contexts augment industry calls for future-capable graduates that can demonstrate enterprise capabilities such as critical thinking, problem-solving, collaboration, and value creation. Gaps between employers’ expectations and evaluations of higher education (HE) graduates’ enterprise capabilities continue to drive university curriculum renewal. There is a particular focus on work-integrated learning (WIL), a spectrum of industry-student engagement activities which provide valuable opportunities for developing and applying skills and knowledge, including enterprise capabilities. Despite small-to-medium enterprises (SMEs) offering fertile ground for enterprise learning, challenges limit their engagement in workplace-based WIL (internships/placements) due to resource and supervisory constraints. This study explores how co-working spaces may support SME engagement in WIL to develop enterprise capabilities, better preparing HE students for future work. It piloted two rounds of business student internships in the largest co-working space in Western Australia, surveying and interviewing both students and workplace supervisors to gauge development and understand enablers and challenges during WIL. Findings affirmed the synergistic value of SMEs and co-working spaces for fostering students’ enterprise capabilities, particularly communication and critical thinking skills, innovative behaviour, and building confidence. While some of the challenges which impact on SMEs engagement and outcomes in WIL remained, the co-working environment offered unique exposure to entrepreneurial mindsets and rich opportunities for collaboration, networking, and formal training. This study offers important insights on WIL design that increases participation among SMEs, a targeted objective of Australia’s national WIL strategy, and leverages co-working space environments to produce future-capable graduates.

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This study compares the efficacy of computer and human analytics in a commemorative setting. Both deductive and inductive reasoning are compared using the same data across both methods. The data comprises 2490 non-repeated, non-dialogical social media comments from the popular touristic site Tripadvisor. Included in the analysis is participant observation at two Anzac commemorative sites, one in Western Australia and one in Northern France. The data is then processed using both Leximancer V4.51 and Dialectic Thematic Analysis. The findings demonstrate artificial intelligence (AI) was incapable of insight beyond metric-driven content analysis. While fully deduced by human analysis the metamodel was only partially deduced by AI. There was also a difference in the ability to induce themes with AI producing anodyne, axiomatic concepts. Contrastingly, human analytics was capable of transcendent themes representing ampliative, phronetic knowledge. The implications of the study suggest (1) tempering the belief that the current iteration of AI can do more than organise, summarise, and visualise data; (2) advocating for the inclusion of preconception and context in thematic analysis, and (3) encouraging a discussion of the appropriateness of using AI in research.

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