This module explores experimental methods used to study human-machine communication, emphasizing the importance of causality, validity, and measurement techniques. It covers how to design experiments, determine appropriate sample sizes, and use various dependent variables such as self-report, behavioral, and physiological measures. Learners will gain skills in evaluating and implementing experimental strategies in human-computer interaction research.

This module explores how physiological and cognitive measures are used to detect and interpret human mental states. It covers the role of psychophysiological signals, such as cardiovascular data and electrodermal activity, in understanding user interactions. Learners will gain insight into how these signals enhance human-machine communication and support personalized system interactions.

This module explores methodological approaches for studying human-machine communication in commercial retail environments. Learners will gain insights into case study methods, data collection techniques, and the challenges of analyzing interactions between humans, AI systems, and cloud-based technologies.

This module explores visual research methods used in human-machine communication, covering mental models, network visualizations, and ethical considerations. Learners will gain an understanding of how to effectively represent and interpret complex data in research contexts, as well as navigate the ethical challenges that arise with machine involvement.

This module explores unobtrusive data collection methods in human-machine communication, focusing on improving validity and understanding interaction patterns through nonreactive observation. It examines how robots and machines can serve as observers and the implications of their presence on research outcomes. Learners will gain insights into designing and evaluating observational studies in HMC contexts.

This module explores how ethnographic methods can be used to study communication and meaning-making in software development communities. It covers the intersection of technical practices and social dynamics, and examines how values and epistemic differences shape collaborative work. Learners will gain insights into conducting research in distributed, technology-driven environments.

This module explores ethnographic research methods for understanding how humans interact with machines in real-world settings. It covers observational techniques, cultural analysis, and the importance of context in human-machine communication. Learners will gain insights into designing and interpreting ethnographic studies in technology-driven environments.

This module explores interview methodologies in Human-Machine Communication (HMC), focusing on the challenges of understanding how people conceptualize and discuss interactions with AI and other communicative technologies. Learners will gain insights into the methodological considerations and theoretical debates that shape this emerging field.

This module explores interdisciplinary approaches to human-machine communication, focusing on feminist, postcolonial, and crip methodologies. It equips learners with tools to analyze and critique the cultural, social, and ethical dimensions of human-machine interactions. By examining theoretical frameworks, students will develop a deeper understanding of inclusivity and representation in technology design.

This module explores ethical frameworks for human-machine communication, focusing on research ethics, gaps in current guidelines, and the integration of multidisciplinary principles. Learners will gain insights into how ethical standards can be adapted for emerging technologies and human-robot interactions. The module also examines ongoing dialogues around ethical review processes in research.

This module explores the evolution and redefinition of affordance theory, highlighting its applications in human-machine communication research. It delves into the conceptual shifts from ecological psychology to design thinking and introduces the idea of imagined affordances. Learners will gain a deeper understanding of how perception, cognition, and design influence human-technology interactions.

This module explores the intersection of emotion and human-machine communication, focusing on how social robots elicit and respond to human emotions. It covers key theories, methodological approaches, and challenges in designing emotionally intelligent interactions. Learners will gain insights into affective computing and its practical applications in human-robot interactions.

This module explores the concept of social presence in human-machine communication, focusing on how users perceive and interact with artificial social actors. It covers theoretical frameworks, ethical considerations, and the impact of emerging technologies on social interactions.

This module examines the principles of interpersonal communication and how they apply to interactions between humans and machines. It explores theories, axioms, and practical examples of how humans and machines can engage in meaningful, natural communication. Learners will gain insight into the social and psychological dimensions of human-machine dialogue.

This module explores how human cognition interacts with social machines through dual-process theories, covering fast and slow thinking, social-cognitive processes, and emotional responses. Learners will gain insight into how humans interpret and engage with artificial entities that simulate social behavior.

This module explores the intersection of privacy and human-machine communication, focusing on the unique challenges posed by smart technologies. It examines theoretical frameworks, ethical considerations, and practical implications of these systems in everyday life. Learners will gain insights into how to approach the design of ethically responsible human-machine interactions.

This module introduces the fundamentals of natural language processing, covering its role in human-computer interaction, key techniques like preprocessing and semantic role labeling, and applications in AI-driven communication systems.

This module explores the impact of datafication on human-machine communication, focusing on ethical challenges, representational biases, and the design of inclusive systems. It examines how voice assistants reflect or challenge gender norms through data-driven processes and highlights the importance of equitable data practices. Learners will gain insights into the intersection of technology, gender, and data ethics.

This module explores how technologies become integrated into daily life, focusing on their symbolic and social roles within domestic settings. It examines theoretical frameworks like the domestication approach and applies them to modern technologies such as IoT and smart devices. Learners will gain insights into how technology shapes and is shaped by everyday routines and cultural meanings.

This module explores how intersectionality challenges the assumptions of neutrality and universality in AI systems. It examines the role of identity, power, and bias in human-machine communication, and how these factors shape the design and use of artificial intelligence. Learners will develop critical insights into the ethical and social implications of AI technologies.

This module explores the concept of data colonialism, its historical roots, and its implications for human-machine communication. It examines how datafication perpetuates systemic inequalities and introduces critical frameworks for addressing algorithmic bias and power imbalances. Learners will develop an understanding of postcolonial perspectives on big data and its impact on marginalized communities.

This module explores the intersection of feminist theory and technology design, focusing on how digital tools can perpetuate inequality and how inclusive frameworks can foster more equitable human-computer interactions. Learners will analyze design biases, examine the impact of cultural contexts on technology use, and consider strategies for creating more just digital systems.

This module explores how disability challenges traditional notions of human-machine communication, examines historical and societal biases, and highlights transformative approaches to inclusive technology design. Learners will gain insights into redefining norms and developing more equitable technological solutions.

This module explores the intersection of robotics and art, focusing on how robotic systems generate aesthetic outcomes and challenge traditional communication methods. Learners will examine interdisciplinary approaches, the role of failure in creative processes, and the evolving relationship between humans, robots, and technology.

This module explores the intersection of labor, automation, and human-machine communication, focusing on how AI and machine learning reshape workplace dynamics, data practices, and the adaptation of technology to human needs.

This module explores the intersection of human-machine collaboration, infrastructure, and labor, focusing on how automation impacts information systems and democratic processes. It delves into theoretical frameworks like Marx's labor analysis and examines real-world examples such as automated journalism. Learners will develop a critical understanding of the social and economic implications of technology in modern work environments.

This module explores how deception operates in human-machine communication, examining design strategies, ethical dilemmas, and user perceptions of AI. It delves into real-world examples and theoretical frameworks to help learners understand the complexities of trust and transparency in AI interactions.

This module explores how regulatory frameworks and social norms influence human-robot interactions, focusing on non-physical harms such as behavioral manipulation. Learners will examine policy gaps and emerging research on the social dimensions of collaborative robotics. The module equips students with the knowledge to assess ethical challenges in robot design and governance.

This module explores the legal and ethical challenges of liability in human-machine communication, including how agency theory applies to autonomous systems. It examines real-world examples such as chatbots and legislative responses like the BOT Act. Learners will gain an understanding of responsibility, control, and accountability in technology-driven interactions.

This module explores the cultural roots of robot communication in Japan, focusing on how manga and anime shape societal perceptions of robots. Learners will analyze the evolution of robot characters and their dual roles as both mechanical tools and emotional companions. The module also examines the impact of key figures like Osamu Tezuka on robot identity in Japanese pop culture.

This module explores the evolving nature of human-robot interactions, examining different types of relationships, ethical considerations, and theoretical frameworks that shape these dynamics. Learners will gain insights into how social robots are perceived and integrated into human society, along with the moral dilemmas they present.

This module explores the concept of algorithms as a form of human-machine communication, examining their social, technical, and interpretive dimensions. Learners will gain insight into how algorithms convey meaning and how humans engage with them in various contexts. The module emphasizes the importance of listening to algorithms and understanding their broader implications.

This module explores the mechanisms and implications of bot-to-bot communication, focusing on how machines interact with each other and with humans. It covers the technical and relational aspects of automated systems, including M2M and HMC integration, and examines the evolving role of bots in collaborative digital environments.