Research into explainable artificial intelligence (XAI) methods has exploded over the past five years. It is essential to synthesize and categorize this research and, for this purpose, multiple systematic reviews on XAI mapped out the landscape of the existing methods. To understand how these methods have developed and been applied and what evidence has been accumulated through model training and analysis, we carried out a tertiary literature review that takes as input systematic literature reviews published between 1992 and 2023. We evaluated 40 systematic literature review papers and presented binary tabular overviews of researched XAI methods and their respective characteristics, such as the scope, scale, input data, explanation data, and machine learning models researched. We identified seven distinct characteristics and organized them into twelve specific categories, culminating in the creation of comprehensive research grids. Within these research grids, we systematically documented the presence or absence of research mentions for each pairing of characteristic and category. We identified 14 combinations that are open to research. Our findings reveal a significant gap, particularly in categories like the cross-section of feature graphs and numerical data, which appear to be notably absent or insufficiently addressed in the existing body of research and thus represent a future research road map.

Agent-based modelling (ABM) is one of the most widely applied computational modelling research techniques in archaeology, because it is well suited to the questions asked by archaeologists. However, the access to vocational training for ABM is limited. In this paper we present the Open Educational Resources (OER) that we produced in an international cooperation. For this purpose, we created online and open tutorials for ABM for archaeologists. In addition, how-to guides and support material were created, both for teachers and students. The materials adhere to international (European) standards in relation to education and the Digital Skills Passport for archaeology. The teaching materials have been well tested using a diverse group of archaeologists during various international conferences and meetings. It was shown that the knowledge and skills of the participants in relation to ABM improved. In addition, the demand for the teaching materials and the workshops was high, ensuring that the tutorials on ABM will be used in the future.

This study aims to develop and demonstrate a sensor-based approach for evaluating effects of smart cycling technologies on in-ride pleasantness. This approach is developed by applying it to the evaluation of a multimodal safety warning system in real-world e-bike riding. An outdoor experiment was conducted using a custom system that warned cyclists about unsafe speeds and evidence-based high-risk zones through visual, vibrotactile, and audio signals. Twenty-nine participants rode with the system while physiological responses, riding dynamics, contextual variables, momentary pleasantness ratings, and warning system performance were measured using mobile sensors, experience sampling, and a post-ride questionnaire. These multimodal data were integrated and analysed using mixed-effects regression and machine-learning classification. Features of Electrodermal Activity, both phasic and tonic, showed associations with pleasantness, whereas features of Heart Rate Variability did not demonstrate significant relationships, highlighting the need for further research into physiological-experiential links. Regarding system impact, visual and vibrotactile warnings were rated as less pleasant but more effective in reducing speed compared to visual and audio signals. By combining affective computing methods with transport safety research, this study provides interdisciplinary guidance for sensor-based, experience-focused evaluation of smart cycling interventions in real-world contexts.