The rise of artificial intelligence (AI) challenges us to explore whether human-to-human relationships can extend to AI, potentially reshaping the future of coaching. The purpose of this study was to examine client perceptions of being coached by a simulated AI coach, who was embodied as a vocally conversational live-motion avatar, compared to client perceptions of a human coach. It explored if and how client ratings of coaching process measures and outcome measures aligned between the two coach treatments. In this mixed methods randomized controlled trial (RCT), 81 graduate students enrolled in the study and identified a personally relevant goal to pursue. The study deployed an alternative-treatments between-subjects design, with one-third of participants receiving coaching from simulated AI coaches, another third engaging with seasoned human coaches, and the rest forming the control group. Both treatment groups had one 60-minute session guided by the CLEAR (contract, listen, explore, action, review) coaching model to support each person to gain clarity about their goal and identify specific behaviors that could help each make progress towards their goal. Quantitative data were captured through three surveys and qualitative input was captured through open-ended survey questions and 27 debrief interviews. The study utilized a Wizard of Oz technique from human-computer interaction research, ingeniously designed to sidestep the rapid obsolescence of technology by simulating an advanced AI coaching experience where participants unknowingly interacted with professional human coaches, enabling the assessment of responses to AI coaching in the absence of fully developed autonomous AI systems. The aim was to glean insights into client reactions to a future, fully autonomous AI with the expert capabilities of a human coach. Contrary to expectations from previous literature, participants did not rate professional human coaches higher than simulated AI coaches in terms of working alliance, session value, or outcomes, which included self-rated competence and goal achievement. In fact, both coached groups made significant progress compared to the control group, with participants convincingly engaging with their respective coaches, as confirmed by a novel believability index. The findings challenge prevailing assumptions about human uniqueness in relation to technology. The rapid advancement of AI suggests a revolutionary shift in coaching, where AI could take on a central and surprisingly effective role, redefining what we thought only human coaches could do and reshaping their role in the age of AI.

The diploma thesis focuses on support of teaching in chosen subjects in the second year of elementary school using interactive white boards with new technology SMART lab. It is focused on evaluation of publicly available interactive teaching portal applications, which occurre in high numbers, however they are obsoled or inappropriately created. Subsequently, the thesis includes a research which pays attention to the usage of the interactive white boards in chosen subjects in the second year of elementary schools located in Vysočina region. The main aim is to ascertain knowledge of teachers when using this technology. The examined subjects were chosen of the result of the questionnaire survey which was sent to teachers from the first grade of the elementary school.

This study was motivated by concerns within the medical education community regarding the psychometric soundness of current assessment methodologies. More specifically, there is reason to seriously question the reliablity and/or validity of these methodologies in assessing the intellectual skills upon which medical competence is based.

<p dir="ltr">Artificial Intelligence (AI) is anticipated to exert a considerable impact on the global Gross Domestic Product (GDP), with projections estimating a contribution of 13 trillion dollars by the year 2030 (IEEE Board of Directors, 2019). In light of this influence on economic, societal, and intellectual realms, it is imperative for Policy Makers to acquaint themselves with the ongoing developments and consequential impacts of AI. The exigency of their preparedness lies in the potential for AI to evolve in unpredicted directions should proactive measures not be promptly instituted.</p><p dir="ltr">This paper endeavors to address a pivotal research question: " Do United States Policy Makers have a sufficient knowledgebase to understand Responsible AI in relation to Machine Learning to pass Artificial Intelligence legislation; and if they do not, how should a pedological instructional framework be created to give them the necessary knowledge?" The pursuit of answers to this question unfolded through the systematic review, gap analysis, and formulation of an instructional framework specifically tailored to elucidate the intricacies of Machine Learning. The findings of this study underscore the imperative for policymakers to undergo educational initiatives in the realm of artificial intelligence. Such educational interventions are deemed essential to empower policymakers with the requisite understanding for formulating effective regulatory frameworks that ensure the development of Responsible AI. The ethical dimensions inherent in this technological landscape warrant consideration, and policymakers must be equipped with the necessary cognitive tools to navigate these ethical quandaries adeptly.</p><p dir="ltr">In response to this exigency, the present study has undertaken the design and development of an instructional framework. This framework is conceived as a strategic intervention to address the evident cognitive gap existing among policymakers concerning the nuances of AI. By imparting an understanding of AI-related concepts, the framework aspires to cultivate a more informed and discerning governance ethos among policymakers, thus contributing to the responsible and ethical deployment of AI technologies.</p>

The main aim of this diploma thesis is to chart the trend of m-learning in Czech schools and to find out, if m-learning influences student' information behavior and how do they use mobile devices in such process. Basic theoretical concepts are defined in first chapters, for example m-learning itself, information behavior and other. Thesis as well contains chapter, where are described different definition of what digital generation is and who are its members. These definitons are the basic theoretical framework of this diploma thesis. One chapter desribes specific projects, statistics and web pages that are dealing with m-learning. Practical part consists of questionnaire evaluation. Survey results are included in the last chapter.