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Current pedagogical approaches to science and technology policy studies in southern African universities may fail to provide students with the necessary capabilities to be effective as innovation policy practitioners. This study investigated whether consideration of epistemic diversity and the situatedness of knowledge could enhance student outcomes in terms of agency and self-efficacy. A pedagogical intervention was designed and implemented for a postgraduate course in engineering economics, following which the students rated their experiences through an online questionnaire. The COVID-19 pandemic partly disrupted the intervention, but also accelerated the use of blended learning as a means of mitigating the harmful effects of the consequent lockdown. The study revealed that most respondents felt that the intervention had helped them to feel more confident, respected and included. However, blended learning could not prevent the stressful experiences due to forced sheltering at home. The results support the importance of finding innovative ways to transform curricula and teaching methods in science and technology studies. Pedagogy based on the principles of epistemic justice, essential to ensure that students from different backgrounds have equal opportunities for personal growth, is challenging under remote learning conditions.

The current article used real data to demonstrate the analysis and synthesis of Mixed Methods Research (MMR) data with generative Artificial Intelligence (Gen AI). I explore how reliable and valid Gen AI data outputs are and how to improve their use. The current content is geared towards enhancing methodological application regardless of field or discipline and includes access to a prompt library and examples of using outputs. The demonstration data used emanated from a study done in South Africa, with a quantitative sample size of 969 first-year engineering students and, for the qualitative part, 14 first-year students. In the current article, I compare my original analysis to ChatGPT results. Generative AI as a mind tool is best used with human insight, and I found this to be especially true when coding qualitative data. ChatGPT produced generic codes if asked to do inductive coding, and the results improved when training the Gen AI on human examples, which led to moderate and significant correlations between human and machine coding. The quantitative analysis was accurate for the descriptive statistics, but the researcher had to use best judgment to select the correct inferential analysis. Quantitative and qualitative analysis should be conducted separately in generative AI before asking the Chatbot for help with mixed methods results. In the current paper, I give guidelines and a tutorial on how to use chatbots in an ethically responsible and scientifically sound manner for research in social and human sciences. [ABSTRACT FROM AUTHOR]