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Pulse check on discipline degrees for changed status quo is vital to ensure global futures for international enrolments (IEs). While employers spend less on training and more on innovating, can IEs manage time spent wisely and profitably (self-sufficiency) via collecting demand-driven credentials (micro-credentialing, Mc)? Due to limited research on Multidisciplinary, Micro-credentialing (MdMc), communication among stakeholders becomes difficult – there is no sense of self-sufficiency and course crossbreed lags; thus, diploma initiatives rarely succeed. Hence, MdMc aims to generate industry-necessitated, new knowledge hybrids where courses could generate adaptable Md links and intersections towards self-sufficiency. We propose a methodology based on Md content analysis on rapidly deployable knowledge bases suitable for multisector employability: a market survey to identify new knowledge areas. The outcome is to be knowledge mapped to identify gaps in skills required for applications to meet across disciplines. Finding the nature of these gaps intends to present possible knowledge links and intersections among courses. Diagrammatised and textual analysis of self-sufficiency-related benefits that could forge robust faulty-industry partnerships will be discussed – to demonstrate fluidity between credentials and careers. The resulting MdMc rigour model would present avenues for new content, training programmes, and a potential HE-industry manifesto. This MdMc model may offer a quick and dynamic process of epistemic, accessibility and instructional rigour checks to achieve professional currency towards self-sufficiency for IEs.

This chapter provides preliminary evidence to show that financial accounting students would use generative artificial intelligence (AI) tools to improve their learning if given the opportunity to do so by their instructors. Most students who completed the exercises we used in the study did so diligently and modified their answers after using a generative AI tool in a manner that suggests beneficial effects. It appears that the more prior knowledge a student had about the subject matter, the more beneficial was the experience. Pitfalls still exist, however. For example, students without knowledge of the subject matter struggled with crafting queries and judging the efficacy of their answers. Moreover, although a minority, some students tended to duplicate their original answers without utilizing the responses generated by the generative AI tool. Additionally, certain students merely copied the answers generated by the AI tool without providing any additional critique or analysis. Implications for teaching and learning and opportunities for future research are discussed.