C8999 - Psychology of Human-Machine Interactions (1) (LM)

Learning outcomes

By the end of the course, students will be able to analyze main psychological principles underlying human-machine interaction, including cognitive load, mental models, trust dynamics, and cognitive biases. They will apply psychological frameworks to design user-centered interfaces, support digital transformation, and enhance human-AI collaboration. Finally, they will develop practical and critical skills to evaluate the societal implications of human-technology coagency with a specific focus in the workplace setting.

Course contents

Foundations: cognition, workload, and system models

• cognitive load and mental workload: cognitive load theory in HMI; workload measurement (NASA-TLX and other indices); split-attention effect and redundancy principle.
• system frameworks and cognitive analysis: SHELL model; cognitive work analysis (CWA); cognitive task analysis (CTA).
• function allocation and its critiques: Fitts' MABA-MABA

Mental models, perception, and interaction design

• mental models: definitions, applications, and shared mental models for teamwork; Norman's cognitive engineering principles (gulfs of execution and evaluation, mapping, feedback, affordances, conventions).
• psychosocial UX/UI design: Gestalt laws, attention theory, and memory constraints; prototyping and testing methods (Wizard of Oz, A/B testing, think-aloud protocols, cognitive walkthrough); usability testing and chatbot usability scales.

Affective computing

• emotional responses to AI and automation (anger, anxiety, excitement, ambivalence); agency attribution and anthropomorphism; human-likeness in human-robot collaboration and in service provision.
• persuasive technology and choice architecture (adaptive interfaces, contextual persuasion); affective computing principles; emotional-intelligence benchmarking of systems.

Machine psychology and emergent AI behaviour

• machine psychology: methods and paradigms of experimental and behavioural psychology (reasoning and cognitive tasks, personality inventories, decision-making) and administers them to AI systems (especially large language models) to characterise their behaviour, biases, and emergent capabilities.
• sycophancy in AI systems, anthropomorphic conversational markers and implications.

Decision-making, trust, and overreliance

• decision support systems (DSS) in organisations and the workplace; user-centred DSS design and decision quality.
• automation bias, automation surprise, complacency, and overreliance; the "ironies of automation" (Bainbridge) extended to AI (Endsley).
• cognitive offloading and cognitive surrender: delegating cognitive effort to external tools and AI (offloading), and at the extreme over-delegating judgement and abdicating critical evaluation (surrender); effects on memory, metacognition, vigilance, and accountability.
• trust formation, calibration, repair, and transfer: trust evolution over repeated interactions; overtrust and undertrust; trust asymmetry; repair strategies after errors and failures; trust transfer between systems.

Technology acceptance and adoption

• individual acceptance models: the technology acceptance model (TAM — perceived usefulness and perceived ease of use); the unified theory of acceptance and use of technology (UTAUT — performance expectancy, effort expectancy, social influence, facilitating conditions); the intelligent systems technology acceptance model (ISTAM), integrating transparency and trust.
• organisational and contextual frameworks: the technology-organization-environment (TOE) framework; diffusion of innovation (DOI, Rogers).
• organisational guidelines for psychological well-being, safety, and performance in technology integration.

Digital transformation, automation, and work design

• digital transition and maturity: digitization, digitalization, and digital transformation; the digital intensity index (DII); digital maturity in enterprises and SMEs.
• process analysis and optimisation and the limits of automation: Moravec's paradox; the 3D model (dull, dumb, dirty); the "tedious work" paradox.
• automation and job redesign: task reconfiguration, augmentation vs substitution, and job shaping; Autor's "O-ring" principle and Polanyi's paradox (tacit/implicit knowledge); zero-sum vs non-zero-sum framings of human-technology interaction.
• meaningful work, sense-making, work engagement, and organisational rituals in automated environments.

Human skills

• upskilling, reskilling, and cross-skilling; pathways for adapting the workforce to AI-augmented roles.
• deskilling and never-skilling: erosion of existing expertise through over-automation (deskilling), and the failure of novices to ever develop foundational competence when AI performs the task from the outset (never-skilling).
• skill decay, skill mismatch, and skill obsolescence.
• skill gap analysis; psychological factors in skill acquisition and retention.

Human-AI teaming and hybrid team dynamics

• foundations of human-AI collaboration theory; automation vs augmentation paradigms; the centaur model and collaborative patterns; the human-AI agency scale (Stanford).
• shared mental models in human-AI teams.
• hybrid team dynamics: the IPO (input-process-output) model applied to human-AI teams; psychological safety in collaborative environments; communication and coordination in mixed teams.

Leading and managing change in digital contexts

• change management theories; psychological resistance to change; human and organisational factors in the use vs misuse of AI and the prevention of misuse; communication strategies for technology adoption.
• leadership in digital contexts: transformational, orchestrative, and virtual leadership and their implications for the team.

Applied exercises and case studies

• organizational maturity assessments; skills-matrix analysis (ILO and UN "exposure-complementarity" tool); case analyses of successful and failed AI implementations across sectors, linked to national and EU R&I projects.

Readings/Bibliography

Indicative references

This is an indicative list of references. The specific readings required to prepare for the written exam will be provided during the classes.

Hagendorff, T. (2023). Machine psychology: investigating emergent capabilities and behavior in large language models using psychological methods. arXiv:2303.13988.

Sharma, M., Tong, M., Korbak, T., Duvenaud, D., Askell, A., Bowman, S. R., … Perez, E. (2023). Towards understanding sycophancy in language models. arXiv:2310.13548.

Kosch, T., Karolus, J., Zagermann, J., Reiterer, H., Schmidt, A., & Woźniak, P. W. (2023). A survey on measuring cognitive workload in human-computer interaction. ACM Computing Surveys, 55(13s), 1-39.

Dekker, S. W., & Woods, D. D. (2002). MABA-MABA or abracadabra? Progress on human–automation co-ordination. Cognition, Technology & Work, 4, 240-244.

Borders, J., Klein, G., & Besuijen, R. (2024). Mental model matrix: implications for system design and training. Journal of Cognitive Engineering and Decision Making, 18(2), 75-98.

Andrews, R. W., Lilly, J. M., Srivastava, D., & Feigh, K. M. (2023). The role of shared mental models in human-AI teams: a theoretical review. Theoretical Issues in Ergonomics Science, 24(2), 129-175.

Norman, D. A. (2013). The design of everyday things. Basic Books.

Blut, M., Wang, C., Wünderlich, N. V., & Brock, C. (2021). Understanding anthropomorphism in service provision: a meta-analysis of physical robots, chatbots, and other AI. Journal of the Academy of Marketing Science, 49, 632-658.

Crolic, C., Thomaz, F., Hadi, R., & Stephen, A. T. (2022). Blame the bot: anthropomorphism and anger in customer–chatbot interactions. Journal of Marketing, 86(1), 132-148.

Pei, G., Li, H., Lu, Y., Wang, Y., Hua, S., & Li, T. (2024). Affective computing: recent advances, challenges, and future trends. Intelligent Computing, 3, 0076.

Bainbridge, L. (1983). Ironies of automation. In Analysis, design and evaluation of man–machine systems (pp. 129-135). Pergamon.

Endsley, M. R. (2023). Ironies of artificial intelligence. Ergonomics, 66(11), 1656-1668.

Wickens, C. D., Clegg, B. A., Vieane, A. Z., & Sebok, A. L. (2015). Complacency and automation bias in the use of imperfect automation. Human Factors, 57(5), 728-739.

Borsci, S., Malizia, A., Schmettow, M., Van Der Velde, F., Tariverdiyeva, G., Balaji, D., & Chamberlain, A. (2022). The chatbot usability scale: the design and pilot of a usability scale for interaction with AI-based conversational agents. Personal and Ubiquitous Computing, 26, 95-119.

Hertzum, M. (2024). Concurrent or retrospective thinking aloud in usability tests: a meta-analytic review. ACM Transactions on Computer-Human Interaction, 31(3), 1-29.

Henrique, B. M., & Santos Jr, E. (2024). Trust in artificial intelligence: literature review and main path analysis. Computers in Human Behavior: Artificial Humans, 2(1), 100043.

Li, Y., Wu, B., Huang, Y., & Luan, S. (2024). Developing trustworthy artificial intelligence: insights from research on interpersonal, human-automation, and human-AI trust. Frontiers in Psychology, 15, 1382693.

Arora, A. (2023). Moravec's paradox and the fear of job automation in health care. The Lancet, 402(10397), 180-181.

Williams, C. A., Schallmo, D., & Scornavacca, E. (2022). How applicable are digital maturity models to SMEs? A conceptual framework and empirical validation approach. International Journal of Innovation Management, 26(03), 2240010.

Smith, A. C., & Stewart, B. (2011). Organizational rituals: features, functions and mechanisms. International Journal of Management Reviews, 13(2), 113-133.

Autor, D. H. (2015). Why are there still so many jobs? The history and future of workplace automation. Journal of Economic Perspectives, 29(3), 3-30.

Autor, D. (2014). Polanyi's paradox and the shape of employment growth (No. w20485). National Bureau of Economic Research.

Berretta, S., Tausch, A., Ontrup, G., Gilles, B., Peifer, C., & Kluge, A. (2023). Defining human-AI teaming the human-centered way: a scoping review and network analysis. Frontiers in Artificial Intelligence, 6, 1250725.

Schmutz, J. B., Outland, N., Kerstan, S., Georganta, E., & Ulfert, A. S. (2024). AI-teaming: redefining collaboration in the digital era. Current Opinion in Psychology, 101837.

Cooke, N. J., & Lawless, W. F. (2021). Effective human–artificial intelligence teaming. Systems Engineering and Artificial Intelligence, 61-75.

Vorm, E. S., & Combs, D. J. (2022). Integrating transparency, trust, and acceptance: the intelligent systems technology acceptance model (ISTAM). International Journal of Human–Computer Interaction, 38(18-20), 1828-1845.

Awa, H. O., & Ojiabo, O. U. (2016). A model of adoption determinants of ERP within TOE framework. Information Technology & People, 29(4), 901-930.

Lundblad, J. P. (2003). A review and critique of Rogers' diffusion of innovation theory as it applies to organizations. Organization Development Journal, 21(4), 50.

Fraboni, F., Brendel, H., & Pietrantoni, L. (2023). Evaluating organizational guidelines for enhancing psychological well-being, safety, and performance in technology integration. Sustainability, 15(10), 8113.

Ten Have, S., Rijsman, J., ten Have, W., & Westhof, J. (2018). The social psychology of change management: theories and an evidence-based perspective on social and organizational beings. Routledge.

Morandini, S., Fraboni, F., De Angelis, M., Puzzo, G., Giusino, D., & Pietrantoni, L. (2023). The impact of artificial intelligence on workers' skills: upskilling and reskilling in organisations. Informing Science, 26, 39.

Pawar, S., & Dhumal, V. (2024). The role of technology in transforming leadership management practices. Multidisciplinary Reviews, 7(4), 2024066.

Stone, C. (2019). Examining the input, process, output model of team effectiveness, leadership styles, and relational coordination as contributors to a profile of team effectiveness.

Morandini, S., Fraboni, F., Hall, M., Quintana-Amate, S., & Pietrantoni, L. (2025). User perspectives on AI explainability in aerospace manufacturing: a card-sorting study. Frontiers in Organizational Psychology, 3, 1538438.

Teaching methods

In-person teaching methods

• Interactive lectures combining theoretical frameworks with real-world examples. These sessions involve a comprehensive review of the relevant literature, exploration of conceptual issues, and presentation of empirical findings.
• “Wooclap” sessions to test learning acquisition and to increase engagement
• Group exercises using assessment tools and evaluation frameworks
• Live demonstrations and “demos” by students
• Role-playing exercises simulating organizational scenarios
• Flipped classroom approach with scientific papers to be read at at home.

E-learning

• Use of “virtuale.unibo.it” for embedded quizzes and assessments
• Asynchronous quizzes or interviews
• Group projects requiring presentation skills

Ethical conduct: All students are required to adhere to the University of Bologna's ethical code. This includes treating all peers, themselves, the course instructor, and teaching assistant with respect and courtesy, embracing and respecting diversity in all its forms.

Assessment methods

Final written exam

This consists of 40 multiple-choice questions + two open questions. Each question is offering four options (one correct answer, three incorrect). The questions are based on the prescribed reading material and the lecture slides. Students are responsible for sourcing the full text of the papers and chapters listed.

Active participation.

This is evaluated using various behavioral indicators such as asking questions, taking notes, abstaining from unrelated tasks, leveraging prior knowledge and personal experiences, and engaging in both in-class and flipped class group activities. Attendance also contributes to this assessment. This component contributes to additional bonus points.

Teaching tools

The slides used in the lectures will be uploaded to the virtuale.unibo.it platform for further reference after each session.

Office hours

See the website of Luca Pietrantoni