Human-AI co-creativity involves both humans and AI collaborating on a shared creative product as partners. In a creative collaboration, interaction dynamics, such as turn-taking, contribution type, and communication, are the driving forces of the co-creative process. Therefore the interaction model is a critical and essential component for effective co-creative systems. There is relatively little research about interaction design in the co-creativity field, which is reflected in a lack of focus on interaction design in many existing co-creative systems. The primary focus of co-creativity research has been on the abilities of the AI. This paper focuses on the importance of interaction design in co-creative systems with the development of the Co-Creative Framework for Interaction design (COFI) that describes the broad scope of possibilities for interaction design in co-creative systems. Researchers can use COFI for modeling interaction in co-creative systems by exploring alternatives in this design space of interaction. COFI can also be beneficial while investigating and interpreting the interaction design of existing co-creative systems. We coded a dataset of existing 92 co-creative systems using COFI and analyzed the data to show how COFI provides a basis to categorize the interaction models of existing co-creative systems. We identify opportunities to shift the focus of interaction models in co-creativity to enable more communication between the user and AI leading to human-AI partnerships.

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Human-AI co-creativity involves humans and AI collaborating on a shared creative product as partners. In many existing co-creative systems, users communicate with the AI using buttons or sliders. However, typically, the AI in co-creative systems cannot communicate back to humans, limiting their potential to be perceived as partners. This paper starts with an overview of a comparative study with 38 participants to explore the impact of AI-to-human communication on user perception and engagement in co-creative systems and the results show improved collaborative experience and user engagement with the system incorporating AI-to-human communication. The results also demonstrate that users perceive co-creative AI as more reliable, personal and intelligent when it can communicate with the users. The results indicate a need to identify potential ethical issues from an engaging communicating co-creative AI. Later in the paper, we present some potential ethical issues in human-AI co-creation and propose to use participatory design fiction as the research methodology to investigate the ethical issues associated with a co-creative AI that communicates with users.CCS Concepts: • Human-centered computing → User studies.

Despite rapidly growing interest in Computer Science (CS), CS has the second lowest participation rate for women of all science and engineering degrees, according to a recent report by the US National Science Board [16]. The reasons for women's underrepresentation and their experiences may differ from university to university. Universities are very different in terms of types of students, student profiles, students' socio-economic status, race, number of students, admission requirements, and resources. For example, some universities can create gender balance in introductory courses through selective admission. However, typically, admission practices in large public universities are not able to achieve gender balance for CS majors. As a result, there are lower percentages of women in the CS major in large public universities than in selective schools. In addition, large public universities tend to have a larger number of CS students when compared to private universities. To understand women students' experiences and struggles in computer science at a large public university in the United States and to find ways to intervene in favor of gender equity in computer science, we conducted in-depth interviews with women students in introductory programming courses. In this article, we present women students' experiences, struggles, expectations and offer potential interventions based on the findings to encourage women to consider CS as their major and stay in the CS major. Our interview data shows there is a persistent effect of lack of prior programming experience and gender bias that leads to a negative experience for women students in introductory CS courses. This article presents the challenges faced by women in CS and provides their recommendations for attracting and retaining women students in CS at large public universities.