This paper presents a blended learning approach and a study evaluating instruction in a software engineering-related course unit as part of an undergraduate engineering degree program in computing. In the past, the course unit had a lecture-based format. In view of student underachievement and the high course unit dropout rate, a distance-learning system was deployed, where students were allowed to choose between a distance-learning approach driven by a moderate constructivist instructional model or a blended-learning approach. The results of this experience are presented, with the aim of showing the effectiveness of the teaching/learning system deployed compared to the lecture-based system previously in place. The grades earned by students under the new system, following the distance-learning and blended-learning courses, are compared statistically to the grades attained in earlier years in the traditional face-to-face classroom (lecture-based) learning.

Before interacting with real users, developers must be proficient in human–computer interaction (HCI) so as not to exhaust user patience and availability. For that, substantial training and practice are required, but it is costly to create a variety of high-quality HCI training materials. In this context, chat generative pretrained transformer (ChatGPT) and other chatbots based on large language models (LLMs) offer an opportunity to generate training materials of acceptable quality without foregoing specific human characteristics present in real-world scenarios. Personas is a user-centered design method that encompasses fictitious but believable user archetypes to help designers understand and empathize with their target audience during product design. We conducted an exploratory study on the Personas technique, addressing the validity and believability of interviews designed by HCI trainers and answered by ChatGPT-simulated users, which can be used as training material for persona creation. Specifically, we employed ChatGPT to respond to interviews designed by user experience (UX) experts. Two groups, HCI professors and professionals, then evaluated the validity of the generated materials considering quality, usefulness, UX, and ethics. The results show that both groups rated the interviews as believable and helpful for Personas training. However, some concerns about response repetition and low response variability suggested the need for further research on improved prompt design in order to generate more diverse and well-developed responses. The findings of this study provide insight into how HCI trainers can use ChatGPT to help their students master persona creation skills before working with real users in real-world scenarios for the first time.

This monograph lays out a discussion framework for understanding the role of human–computer interaction (HCI) in public policymaking. We take an international view, discussing potential areas for research and application, and their potential for impact. Little has been written about the intersection of HCI and public policy; existing reports typically focus on one specific policy issue or incident. To date, there has been no overarching view of the areas of existing impact and potential impact. We have begun that analysis and argue here that such a global view is needed. Our aims are to provide a solid foundation for discussion, cooperation and collaborative interaction, and to outline future programs of activity. The five sections of this report provide relevant background along with a preliminary version of what we expect to be an evolving framework. Sections 1 and 2 provides an introduction to HCI and public policy. Section 3 discusses how HCI already informs public policy, with representative examples. Section 4 discusses how public policy influences HCI and provides representative public policy areas relevant to HCI, where HCI could have even more impact in the future: (i) laws, regulations, and guidelines for HCI research, (ii) HCI research assessments, (iii) research funding, (iv) laws for interface design — accessibility and language, (v) data privacy laws and regulations, (vi) intellectual property, and (vii) laws and regulations in specific sectors. There is a striking difference between where the HCI community has had impact (Section 3) and the many areas of potential involvement (Section 4). Section 5 a framework for action by the HCI community in public policy internationally. This monograph summarizes the observations and recommendations from a daylong workshop at the CHI 2013 conference in Paris, France. The workshop invited the community's perspectives regarding the intersection of governmental policies, international and domestic standards, recent HCI research discoveries, and emergent considerations and challenges. It also incorporates contributions made after the workshop by workshop participants and by individuals who were unable to participate in the workshop but whose work and interests were highly related and relevant.

Expert systems are built from knowledge traditionally elicited from the human expert. It is precisely knowledge elicitation from the expert that is the bottleneck in expert system construction. On the other hand, a data mining system, which automatically extracts knowledge, needs expert guidance on the successive decisions to be made in each of the system phases. In this context, expert knowledge and data mining discovered knowledge can cooperate, maximizing their individual capabilities: data mining discovered knowledge can be used as a complementary source of knowledge for the expert system, whereas expert knowledge can be used to guide the data mining process. This article summarizes different examples of systems where there is cooperation between expert knowledge and data mining discovered knowledge and reports our experience of such cooperation gathered from a medical diagnosis project called Intelligent Interpretation of Isokinetics Data, which we developed. From that experience, a series of lessons were learned throughout project development. Some of these lessons are generally applicable and others pertain exclusively to certain project types.

Agent-oriented software is an established research field. For this reason, it is important to develop comprehensive measures of excellence to evaluate this software. No set of measures defining the overall quality of an agent has been developed to date. Some attempts at evaluating agent quality have addressed certain agent features, like the development process. We believe that agent quality can be determined as a function of well-defined characteristics. Evaluated using appropriate measures, these characteristics will assure an agent's reliability and correct functionality. This paper deals with an important agent feature, namely, autonomy. Autonomy is considered to be the agent's ability to operate independently, without the need for human guidance or the intervention of external elements. The article proposes a set of measures used to evaluate the autonomy of a agent and presents a case study analyzing the behavior of these measures.