Doctoral School

Supervisors team

Prof. dr hab. . Adam Wierzbicki, Prof. dr hab. . Grzegorz Sędek (SWPS University), Dr. Kinga Skorupska

Contact persons

Prof. dr hab. . Adam Wierzbicki - adamw@pjwstk.edu.pl

Description of the problem

Multi-List recommendation interfaces (MLRIs) are widely used on streaming platforms (such as Netflix) and in e-commerce applications on mobile devices [1,2]. They are used to display the results of recommendation algorithms that are trained based on user profiles and choices.

Research on recommender systems usually treats users' choices made in MLRIs as the real basis (ground truth) used to learn and evaluate recommender algorithms. Recommendation systems are also evaluated using subjective usability, which unfortunately makes it impossible to take into account the influence of individual user characteristics on their choices and their usability.

As a result, neither recommendation algorithms nor the design of MLRIs take into account users' cognitive limitations. For example, older users often have limited working memory, leading to suboptimal choices [3], as well as reduced attention span or an increased need to shut down thoughts, leading to a narrower set of possible choices. Cognitive limitations affect the utility of users' choices and their interaction with MLRIs, and thus the quality of the data used to train recommendation algorithms.

The basic idea of this research project is to better understand user preferences, using methods to obtain preferences based on available information about recommended products (e.g., videos) and latent features. Based on user preferences, we can construct a utility function for each user. Usability functions can be used to solve the cold start problem in recommender systems, because they are available after preferences are obtained, before the system has information about the user's choices. However, we will use personalized user utility functions to evaluate user choices in MLRI, taking into account individual user characteristics such as age or cognitive ability. We hypothesize that users with cognitive limitations will have lower utility of their choices in MLRI.

The research problem is to design a human-AI interaction - using a chatbot, for example - that would improve the usability of a user's MLRI choices. The chatbot could, for example, act as a digital "nudge" or recommendation agent that would intervene when a user's interaction with the MLRI leads to poorer choices.
Part of the research problem is also to redesign recommendation algorithms so that they can account for the user's cognitive limitations and support better user choices in the MLRI.

Bibliography

Loepp, B. and Ziegler, J., 2023. How Users Ride the Carousel: Exploring the Design of Multi-List Recommender Interfaces From a User Perspective. In: Proc. 17th ACM Conf. on Recommender Syst., pp.1090-1095.

Ferrari Dacrema, M., Felicioni, N. and Cremonesi, P., 2022. Offline Evaluation of Recommender Systems in a User Interface With Multiple Carousels. Front. Big Data, 5, 910030.

Pawlowska, J., Rydzewska, K., & Wierzbicki, A. (2023). Using cognitive models to understand and counteract the effect of self-induced bias on recommendation algorithms. Journal of Artificial Intelligence and Soft Computing Research, 13(2), 73-94.

Supervisors team

Prof. dr hab. . Adam Wierzbicki, Dr. Klara Rydzewska (SWPS University), Dr. Kinga Skorupska

Contact persons

Prof. dr hab. . Adam Wierzbicki - adamw@pjwstk.edu.pl, Dr. Kinga Skorupska - skorupska@pjwstk.edu.pl

Description of the problem

Programming anxiety and intellectual helplessness are barriers that negatively affect students' performance, engagement and overall confidence when learning programming. Although intelligent tutoring systems (ITS) have been developed and tested to support students in learning programming, they focus mainly on helping students solve problems rather than dealing with the emotional and cognitive challenges students may face.

The goal of this study is to design and evaluate a human-AI interaction system that supports students in overcoming programming anxiety and intellectual helplessness. Using validated psychological scales, the project will measure levels of programming anxiety and intellectual helplessness among students taking programming courses. The results of these measurements will be correlated with student performance in ITS for programming, providing insight into the relationship between emotional barriers and learning outcomes.

A key component of the study is the development of a conversational agent designed not only to provide technical guidance, but also emotional and cognitive support. The agent will act as a personalized support tool that recognizes signs of frustration, offers adaptive programming advice, and implements features designed to improve students' mood and motivation. Its effectiveness will be tested in controlled, randomized experiments with programming students, measuring its impact on programming anxiety, intellectual helplessness and overall academic performance.

Integrating techniques from the fields of human-computer interaction, psychology and AI-assisted learning, this project aims to improve the educational experience of programming students. The results of this research will contribute to the design of more effective AI-based learning support tools and help develop best practices in reducing anxiety and promoting resilience in computer science education.

Bibliography

Yildirim, O. G., & Ozdener, N. (2022). The Development and Validation of the Programming Anxiety Scale. International Journal of Computer Science Education in Schools, 5(3), n3.

Peng, W., Qin, Z., Hu, Y., Xie, Y., & Li, Y. (2023). Fado: Feedback-aware dual controlling network for emotional support conversation. Knowledge-Based Systems, 264, 110340.

Supervisors team

Prof. dr hab. . Adam Wierzbicki, Prof. dr hab. Izabela Grabowska (Kozminski University), Dr. Pavel Savov

Contact persons

Prof. dr hab. . Adam Wierzbicki - adamw@pjwstk.edu.pl

Description of the problem

The rapidly evolving information and communications technology (ICT) labor market requires a workforce equipped with both traditional and modern skills. The ESCO (European Catalog of Skills, Competencies, Qualifications and Occupations) framework provides a comprehensive classification of the skills and competencies required in various sectors, but matching these skills with academic learning outcomes remains a challenge.

The goal of this research project is to bridge the gap between ESCO skill classifications and academic course outcomes by mapping ESCO-defined ICT skills to the learning objectives of individual academic courses. By doing so, the project aims to ensure that educational programs are in line with industry requirements, which is expected to increase the employability of graduates.

A key component of the study is the automatic generation of competency tests to assess theoretical, practical and higher cognitive skills at various levels of Bloom's taxonomy. This includes critical and creative thinking, which are integral components of ICT competence. Using relevant course literature and large language models, the project will generate questions and corresponding answers tailored to effectively assess these skills.

The project will also explore automatic verification of generated questions and answers, mimicking the performance of established metrics such as TruthfulQA. This will include creating a set of questions and answers manually verified by subject matter experts and comparing them to established question scales, ensuring the methodological soundness of the question generation processes. The verified questions will then be used to assess the competencies of candidates for various ICT occupations by mapping CEDEFOP's occupational profiles to ESCO skills.

Completion of the assessments will be evidenced by micro-certificates that will certify a candidate's proficiency in specific skills or skill sets relevant to occupational roles. By integrating the collected data on the mapping of occupations to ESCO skills and their alignment with learning outcomes data, the project will enable comparative analysis of academic profiles and occupational requirements. Such findings will help improve educational offerings and contribute to a flexible framework that aligns education with labor market needs.

Bibliography

Ward, R., Phillips, O., Bowers, D., Crick, T., Davenport, J. H., Hanna, P., ... & Prickett, T. (2021, April). Towards a 21st century personalized learning skills taxonomy. In 2021 IEEE Global Engineering Education Conference (EDUCON) (pp. 344-354). IEEE.

Pan, X., van Ossenbruggen, J., de Boer, V., & Huang, Z. (2024). A RAG Approach for Generating Competency Questions in Ontology Engineering. arXiv e-prints, arXiv-2409.

Di Nuzzo, D., Vakaj, E., Saadany, H., Grishti, E., & Mihindukulasooriya, N. (2024, September). Automated Generation of Competency Questions Using Large Language Models and Knowledge Graphs. In SEMANTiCS Conference.

Hadzhikoleva, S., Rachovski, T., Ivanov, I., Hadzhikolev, E., & Dimitrov, G. (2024). Automated Test Creation Using Large Language Models: A Practical Application. Applied Sciences, 14(19), 9125.

Cheng, Z., Xu, J., & Jin, H. (2024). Treequestion: Assessing conceptual learning outcomes with llm-generated multiple-choice questions. Proceedings of the ACM on Human-Computer Interaction, 8(CSCW2), 1-29.

Biancini, G., Ferrato, A., & Limongelli, C. (2024, June). Multiple-choice question generation using large language models: Methodology and educator insights. In Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization (pp. 584-590).

Sundar, K., Manohar, E., Vijay, K., & Prakash, S. (2024, October). Revolutionizing Assessment: AI-Powered Evaluation with RAG and LLM Technologies. In 2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS) (pp. 43-48). IEEE.

CEDEFOP mapping of occupations to ESCO skills, https://www.cedefop.europa.eu/files/4172_en.pdf, https://www.cedefop.europa.eu/files/using_learning_outcomes_to_compare_the_profile_of_vet_qualifications_-_a_global_approach_cedefop_unesco_november_2017.pdf 

ESCO skill classification, https://esco.ec.europa.eu/en/classification 

Supervisors team

Prof. dr hab. . Adam Wierzbicki, Prof. Jahna Otterbacher (Open University of Cyprus), Dr. Kinga Skorupska

Contact persons

Prof. dr hab. . Adam Wierzbicki - adamw@pjwstk.edu.pl

Description of the problem

The rise of online disinformation poses a serious threat to public trust, democratic institutions and social cohesion. European agencies and fact-checking organizations such as EUvsDisinfo are actively working to counter disinformation, but scalable solutions based on artificial intelligence remain a challenge. Existing approaches to disinformation detection often rely on hand-crafted datasets and rule-based heuristics that struggle to keep up with evolving disinformation tactics.

The goal of this research project is to develop an AI-based framework for detecting and mitigating disinformation, using machine learning, persuasive communication strategies, and real-world experimental validation. The first goal is to build a structured database of disinformation, identifying key linguistic, structural and contextual features that characterize disinformation. This database will serve as a foundation for identifying disinformation narratives, assessing intent, and detecting manipulative techniques such as emotional framing, false causality, and selective omission of facts.

In addition to detection, this project will explore intervention strategies that warn users of misinformation in social media, particularly in instant messaging contexts (such as Discord or Facebook Messenger), and offer persuasive counter-narratives. Messages generated by artificial intelligence will be designed to explain why a piece of information is misleading, refute false claims and convince users to reject disinformation. The effectiveness of these interventions will be tested in controlled experiments and real-world deployments, such as a course on disinformation detection, measuring their impact on user engagement, trust and susceptibility to disinformation.

To achieve these goals, the project will use a combination of machine learning, natural language processing and user-centered evaluation methods. It will also explore how explainable artificial intelligence can improve trust in disinformation detection systems and how personalized interventions can enhance user trust. The research will contribute to both practical applications in disinformation detection and a theoretical framework for disinformation correction, ultimately supporting policymakers, educators and technology platforms in the fight against online disinformation.

Bibliography

João A. Leite, Olesya Razuvayevskaya, Kalina Bontcheva, and Carolina Scarton. 2024 EUvsDisinfo: A Dataset for Multilingual Detection of Pro-Kremlin Disinformation in News Articles. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM '24). Association for Computing Machinery, New York, NY, USA, 5380-5384. https://doi.org/10.1145/3627673.3679167

Supervisors team

Prof. dr hab. . Adam Wierzbicki, Dr. Styliani Kleanthous (Open University of Cyprus)

Contact persons

Prof. dr hab. . Adam Wierzbicki - adamw@pjwstk.edu.pl

Description of the problem

The purpose of this study is to understand how humans interact with artificial intelligence (AI)-based decision support systems (DSS) in collaborative decision-making tasks. The study focuses on the following questions:

1. Trust dynamics: When do people trust AI recommendations more than their own judgment?
2. Delegating tasks: When and why do humans delegate decisions to AI?
3. Social influence and behavioral adaptation: How does AI affect human decisions and team dynamics?

Human-AI (HAI) collaboration is increasingly shaping decision-making processes in areas such as healthcare, finance and logistics. Decision support systems (DSS) enriched with AI capabilities aim to support human judgment by providing data-driven recommendations. However, the effectiveness of these systems depends on how users perceive, trust and interact with AI-generated advice. Previous research has shown that trust in AI depends on predictability, reliability and transparency [7], and individuals often tend to either mistrust (where they over-rely on AI) or resist algorithms (where they reject AI recommendations after encountering errors) [9]. To ensure optimal trust calibration, it is important to study the conditions under which people accept or reject AI decisions, especially in contexts with high risk or uncertainty [2].

In addition, a key challenge in HAI interactions is task delegation - figuring out when and why people delegate control to AI systems. Sociopsychological theories suggest that people delegate tasks or rely on AI under certain conditions. Cognitive load theory [10] suggests that people are more likely to rely on AI when they experience high cognitive load, such as when making decisions in fast-paced situations or with complex decision-making tasks [6]. However, task delegation is not solely driven by cognitive limitations. For example, Social Comparison Theory [5] indicates that individuals adjust their decisions based on AI recommendations, especially when they lack confidence in their own judgment.

According to Social Influence Theory [3], AI can act as a persuasive agent, shaping the user's decisions through authority fallacy (trusting AI as an expert) or consensus heuristics (conforming to AI's recommendation when it is perceived as the dominant opinion) [11]. Therefore, it is important to understand the interaction of trust, delegation and social influence dynamics between the two entities, in the context of the theories mentioned above. Understanding the mechanisms that drive human trust, task delegation and decision adaptation in AI-based decision support systems is crucial to designing systems that support, rather than replace, human judgment. The theory of planned behavior [1] provides a useful perspective for studying how perceived usefulness, ease of use and social norms influence people's reliance on AI [8]. Research has shown that users tend to trust AI more when explanations are provided (Binns et al., 2018), but excessive detail can lead to cognitive overload, reducing trust and engagement [4].

This project investigates how trust dynamics, task delegation and social influence mechanisms affect human-AI cooperation in DSS systems, using user studies to analyze behavior in different decision-making contexts. The project aims to systematically investigate how social influence and cognitive load interact to influence task delegation and reliance on AI-based DSSs, testing whether different explanatory styles and AI confidence levels influence user behavior. Additionally, the project aims to test experimentally how explainability, social influence and task complexity interact to shape human-AI cooperation. Using user research (e.g., crowdsourcing), we will realize interactive decision-making scenarios in different domains, measuring how trust evolves over time and under different experimental conditions. By identifying key factors influencing trust calibration, delegation and decision-making behavior, this research will contribute to the development of more effective, transparent and human-centered AI-based DSS systems. In addition, we will evaluate the ability of AI systems to support interactive decision-making in educational settings.

Research questions that can be addressed:

RQ4: How do AI explanations affect human-AI cooperation?

RQ1: When do people trust AI recommendations more than their own decisions?

RQ2: How does AI affect human decision-making behavior over time?

RQ3: In what situations do people delegate decisions to AI instead of making them themselves?

Bibliography

[1] Icek Ajzen. 1991. the Theory of planned behavior. Organizational Behavior and Human Decision Processes (1991).

[2] Gagan Bansal, Tongshuang Wu, Joyce Zhou, Raymond Fok, Besmira Nushi, Ece Kamar, Marco Tulio Ribeiro, and Daniel Weld. 2021 Does the whole exceed its parts? the effect of ai explanations on complementary team performance. In Proceedings of the 2021 CHI conference on human factors in computing systems. 1-16.

[3] Robert B Cialdini and Noah J Goldstein. 2004. social influence: compliance and conformity. Annu. Rev. Psychol. 55, 1 (2004), 591-621.

[4] Motahhare Eslami, Sneha R Krishna Kumaran, Christian Sandvig, and Karrie Karahalios. 2018. communicating algorithmic process in online behavioral advertising. In Proceedings of the 2018 CHI conference on human factors in computing systems. 1-13.

[5] Leon Festinger. 1954. a theory of social comparison processes. Human relations 7, 2 (1954), 117-140.

[6] Kate Goddard, Abdul Roudsari, and Jeremy C Wyatt. 2012. automation bias: a systematic review of frequency, effect mediators, and mitigators. Journal of the American Medical Informatics Association 19, 1 (2012), 121-127.

[7] John D Lee and Katrina A See. 2004. trust in automation: designing for appropriate reliance. Human factors 46, 1 (2004), 50-80.

[8] Jennifer M Logg, Julia A Minson, and Don A Moore. 2019. algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151 (2019), 90-103.

[9] Raja Parasuraman and Victor Riley. 1997. humans and automation: use, misuse, disuse, abuse. Human factors 39, 2 (1997), 230-253.

[10] John Sweller. 1988. cognitive load during problem solving: Effects on learning. Cognitive science 12, 2 (1988), 257-285.

[11] Baobao Zhang and Allan Dafoe. 2020. US public opinion on the governance of artificial intelligence. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society. 187-193

Supervisors team

Prof. dr hab. . Adam Wierzbicki, Dr. Rafal Rzepka (Hokkaido University)

Contact persons

Prof. dr hab. . Adam Wierzbicki - adamw@pjwstk.edu.pl

Description of the problem

The capabilities of large language models (LLMs) are being tested in a wide range of aspects, including those related to truthfulness, security, fairness, resilience, privacy or machine ethics [1,2]. However, the topic of trust, due to the time-consuming nature of the required interaction and controllability issues, is difficult to study. This problem can be addressed by using LLM to simulate environments in which multiple agents can not only communicate in natural language, but also take (or propose) actions [3,4]. The purpose of such agents would be to advise human users on behavior in situations that require an understanding of trust or distrust. For example, a user might ask his agent: "X told me that I should buy shares in company Y. Should I trust this advice?" or "According to Y, I should demand a raise from my boss. Is Y right?" or even just "Z wants me to go with him on a trip to Mallorca next weekend. Should I go?" AI agents could try to answer these questions by assessing people's trustworthiness, for example by asking the user: "Do you trust X for advice on the stock market?". To accomplish this, AI agents may have to rely on information from other AI agents; this in turn raises questions about the trustworthiness of the AI agents themselves. Trust is particularly important in situations where decisions must be made under uncertainty, due to incomplete information or uncertain user or agent behavior. We will seek to create an experimental environment that replicates such a situation to explore how AI agents understand trust.

Research components and methods

Implementation of Trust and Fairness Algorithms
Many computational frameworks for trust have been proposed [5]. In this study, we will implement several computational methods for evaluating trust in multi-agent scenarios. This includes the development of metrics to measure the level of trust in natural language interactions, the creation of algorithms to detect trust violations and repairs, and the implementation of fairness constraints in optimizing agent behavior. The system will track the evolution of trust using linguistic and behavioral metrics. In addition to measuring changes in trust, experiments will be conducted to study the relationship between morality and trust.

Dialogue Pattern Analysis Framework
We will develop natural language processing pipelines specifically designed to identify trust-related dialogue features. This will include the creation of annotation schemes for trust markers in a conversation, the implementation of sentiment analysis focused on trust dynamics, and the development of methods for tracking changes in trust status as the dialogue unfolds. The framework will combine rule-based and neural approaches for pattern recognition. We will evaluate the proposed framework in multiple languages (for example, English, Polish or Japanese), aiming to design a universal trust assessment framework that takes into account language and cultural differences. The study will focus on identifying both universal and language-specific trust-building patterns.

The Trust Measurement System
Assessment Framework will combine various measurement approaches: automated linguistic analysis of trust indicators, behavioral pattern recognition, and performance-based assessment. We will develop specific metrics to measure the speed, stability and resilience of trust formation to disruption. The system will include methods for conducting comparative analyses between different scenarios and agent configurations.

Strategy Generalization Evaluation
To study the adaptability of agents, we will implement systematic perturbation methods to test strategy resilience. This includes developing protocols for introducing unexpected events, measuring response effectiveness, and analyzing strategy transfer in different contexts. The evaluation will track both immediate adaptations and the long-term evolution of the strategy.

Bibliography

[1] Huang, Yue, et al. "TrustLLM: Trustworthiness in Large Language Models." Proceedings of the 41st International Conference on Machine Learning (2024).

[2] Liu, Yang, et al. "Trustworthy LLMs: A survey and guideline for evaluating large language models' alignment." arXiv preprint arXiv:2308.05374 (2023).

[3] Gandhi, Kanishk, et al. "Understanding social reasoning in language models with language models." Advances in Neural Information Processing Systems 36 (2024).

[4] Park, Joon Sung, et al. "Generative agents: Interactive simulacra of human behavior." Proceedings of the 36th annual ACM symposium on user interface software and technology. 2023.

[5] Wierzbicki, Adam. Trust and fairness in open, distributed systems. Vol. 298. Berlin: Springer, 2010.

Supervisors team

Prof. dr hab. . Adam Wierzbicki, Prof. Joemon Jose (University of Glasgow)

Contact persons

Prof. dr hab. . Adam Wierzbicki - adamw@pjwstk.edu.pl

Description of the problem

The present study aims to investigate improvements in the effectiveness of AI agent collaboration, particularly in the context of exploratory search tasks. Exploratory search is closely related to the basic human activity of seeking information [1]. In such scenarios, users often engage without a clear goal or with loosely defined objectives. As a result, search intentions develop dynamically, shaped by users' experiences and the knowledge they gain in the process. Exploratory search thus involves understanding diverse information needs, identifying their interdependencies and actively constructing search solutions. Multi-agent systems represent a promising approach to solving such tasks. The main challenge, however, is to enable learning agents to make adaptive decisions in complex and dynamic search scenarios. Adaptive decision-making involves selecting actions, evaluating their results, and using these evaluations to effectively achieve higher states of reward.

The purpose of this study is to explore how AI agents can mimic and improve the adaptive and evolving strategies used in human information-seeking tasks. Exploratory search represents a high uncertainty scenario in which relatively little is understood about the cognitive processes that drive decision-making in such contexts [2]. Effective decision-making in these scenarios requires considerable knowledge of the environment/needs, which can only be gained through exploration. The main challenge is to understand and formalize the cognitive mechanisms that underlie these processes, and to use these insights to develop solutions for agent-based interactions.

Our goal is to design a human-AI interaction procedure that will allow AI agents to report the results of search sessions, get feedback from users, and use this feedback to adjust their search strategy. This interaction procedure goes beyond the concept of suggestion, as it emphasizes the role of generative AI in summarizing search results and asking the user for feedback and further search instructions. In this way, the roles of the AI agent and the human agent can be reversed.

We will focus on the application of the proposed AI-supported open search system in educational environments, allowing users to explore a predefined set of knowledge. By evaluating users' learning outcomes over time, we will be able to assess the performance of exploratory search and its usefulness in different learning scenarios and learning styles.

Bibliography

Ryen W. White. Interactions with Search Systems. Cambridge University Press, 2016. ISBN: 978-1-107-03422-8.

Gloria Cecchini, Michael DePass, Emre Baspinar, Marta Andujar, Surabhi Ramawat, Pierpaolo Pani, Stefano Ferraina, Alain Destexhe, Rubén Moreno-Bote, Ignasi Cos, Cognitive mechanisms of learning in sequential decision-making under uncertainty: an experimental and theoretical approach, Frontiers in Behavioral Neuroscience, 2024.

Han, S., Zhang, Q., Yao, Y., Jin, W., Xu, Z., & He, C. (2024). LLM Multi-Agent Systems: Challenges and Open Problems. arXiv preprint arXiv:2402.03578.

Supervisors team

Prof. Adam Wierzbicki, Ph. dr hab. ., Bartosz Muczynski, Ph.D. (Maritime University of Technology in Szczecin), Kinga Skorupska, Ph.

Contact persons

Prof. dr hab. . Adam Wierzbicki - adamw@pjwstk.edu.pl, Dr. Kinga Skorupska - skorupska@pjwstk.edu.pl

Description of the problem

The growing popularity of distributed e-learning experiences and augmented reality (XR)-based courses, combined with the increasing availability of XR solutions, suggests that more and more educational institutions will integrate this technology into their curricula in the future. However, there are currently no comprehensive accessibility guidelines and best practices for the design, development and use of XR hardware and software in distance education, which is a significant gap in ensuring inclusivity for all students.

The integration of artificial intelligence (AI) in remote learning represents an opportunity to improve accessibility for students with different needs in a variety of e-learning environments. AI-enabled technologies can break down barriers for learners, not only by providing adaptive learning experiences tailored to individual requirements, but also by enabling full participation by addressing their specific accessibility needs, both in e-learning platforms and immersive virtual environments (IVRs).

The purpose of this study is to explore how AI-supported technologies can improve accessibility in distance education in XR environments, promoting inclusivity. Through the use of AI-supported solutions such as predictive analytics, personalized learning and adaptive interfaces, this topic aims to address existing challenges and propose an AI-based framework for accessible distance education.

Potential general hypotheses:

H1: Personalized AI teachers improve the academic performance of neurodiverse students by tailoring educational content to their cognitive preferences. 

H2: Augmented reality (XR) environments enhanced with AI-supported accessibility features can improve the learning experience of students with sensory challenges and other difficulties, and, through the curb effect, improve learning outcomes for all students. 

H3: AI-supported predictive analytics can identify accessibility challenges in real time and proactively provide tailored support to individual students.

By exploring these hypotheses, this topic aims to contribute to the development of inclusive AI-supported remote learning technologies and provide practical guidelines for integrating accessibility into XR-based education, providing equal educational opportunities for a wider range of students.

Bibliography

Bartosz Muczynski, Kinga Skorupska , Katarzyna Abramczuk, Cezary Biele, Zbigniew Bohdanowicz, Daniel Cnotkowski, Jazmin Collins, Wieslaw Kopeć, Jarosław Kowalski, Grzegorz Pochwatko, and Thomas Logan. 2023. VR Accessibility in Distance Adult Education. In Human-Computer Interaction - INTERACT 2023: 19th IFIP TC13 International Conference, York, UK, August 28 - September 1, 2023, Proceedings, Part IV. Springer-Verlag, Berlin, Heidelberg, 685-691. https://doi.org/10.1007/978-3-031-42293-5_94

Katarzyna Abramchuk, Zbigniew Bohdanowicz , Bartosz Muczynski, Kinga H. Skorupska, and Daniel Cnotkowski. 2023 Meet me in VR! Can VR space help remote teams connect: A seven-week study with Horizon Workrooms. International Journal of Human-Computer Studies 179, 103104. DOI:https://doi.org/10.1016/j.ijhcs.2023.103104.

Geriş, A. 2024. The EVRIM Framework: Guiding Ethical and Inclusive Virtual Reality Integration in Education. Manisa Celal Bayar Üniversitesi Eğitim Fakültesi Dergisi, 12(2), Article 2. https://doi.org/10.52826/mcbuefd.1511454 

Killough, D., Ji, T. F., Zhang, K., Hu, Y., Huang, Y., Du, R., & Zhao, Y. 2024. XR for All: Understanding Developer Perspectives on Accessibility Integration in Extended Reality (arXiv:2412.16321). arXiv. https://doi.org/10.48550/arXiv.2412.16321

Supervisors team

Adam Kuzdralinski, Ph.dr hab. ., Wieslaw Kopeć, Ph.D., Grzegorz Pochwatko, Ph.D. (VR Laboratory of the Institute of Psychology of the Polish Academy of Sciences), Tommy Nilsson, Ph.D. (European Space Agency, European Astronaut Centre)

Contact persons

Dr. Wieslaw Kopeć - kopec@pjwstk.edu.pl

Description of the problem

PJAIT 's XR Center conducts research on immersive systems in the context of human performance and human factors research, particularly in space exploration and isolation conditions under the Alpha-XR project, as well as its real-world applications, including training, selection and teleoperation [1].

Augmented Reality (XR) immersive systems, including Virtual Reality (VR) and Augmented Reality (AR), are increasingly being used in human performance research in the context of space exploration, such as as part of ESA's recommendations to research teams on spatial analogs and human performance, as well as in various fields such as training, selection and skill acquisition [1,2]. These systems offer controlled environments and precise measurement capabilities that enable researchers to analyze human behavior and performance in realistic scenarios. However, the effectiveness of XR-based research is highly dependent on user experience, especially when using haptic interfaces linked to interactions with physical objects.

Current research often focuses on the technical aspects of haptic feedback, neglecting the cognitive and motor demands placed on users. For example, complex haptic patterns and interactions can cause excessive cognitive load, impeding learning and performance, and have negative effects on well-being in isolation [3]. Moreover, individual differences in motor skill acquisition and sensory processing can affect a user's ability to effectively interpret and use haptic feedback.

For this transdisciplinary research topic, there are several key research areas related to the study, development and improvement of the user experience of XR immersion systems in human performance research, including:

1. Optimizing Haptic Interface Design: Investigate the impact of various haptic interface parameters (e.g., force feedback, vibration patterns, texture simulation) and custom controllers, as well as various methods of mixing physical objects with virtual elements on cognitive load, motor skill acquisition, and user engagement. Design and evaluate adaptive haptic feedback strategies that adjust to individual user characteristics and task requirements.
2. Development of User-Directed Interaction Models: Develop user-directed interaction models that account for cognitive limitations, variability in motor skills, and differences in sensory processing. These models will be used to design intuitive and effective XR interfaces that minimize cognitive load and maximize learning outcomes.
3. Quantifying Cognitive Load and Motor Skill Acquisition: Develop and validate methodologies for quantifying cognitive load and motor skill acquisition in XR environments using various haptic interfaces. This includes the use of psychophysiological measures (e.g., EEG, heart rate variability), behavioral data (e.g., movement tracking, task performance), and subjective assessments (e.g., questionnaires, interviews).
4. Investigating the Impact of Individual Differences: Investigating how individual differences, such as age, prior experience and cognitive ability, affect user experience and performance in XR-based human performance studies.

The main hypothesis is that optimizing haptic interface design and interaction models based on user-directed principles will significantly improve user experience, increase motor skill acquisition and reduce cognitive load in immersive XR training systems. The research problem involves a multidisciplinary approach, combining a strong understanding of XR systems (both software and content development) with human-computer interaction, cognitive psychology, motor control and haptic technology. This research will contribute to the development of more effective and accessible XR-based human performance research within PJAIT 's XR Center and the Human Aspects in Science and Engineering (HASE) research group. 

Bibliography

Kopeć, W., Pochwatko, G., Kornacka, M., et al.(2024). Human Factors in Space Exploration: Opportunities for International and Interdisciplinary Collaboration. In: Biele, C., et al. Digital Interaction and Machine Intelligence. MIDI 2023. Lecture Notes in Networks and Systems, vol 1076. Springer, Cham. https://doi.org/10.1007/978-3-031-66594-3_35

De la Torre, Kopeć, W. et al. Space Analogs and Behavioral Health Performance Research review and recommendations checklist from ESA Topical Team. npj Microgravity 10, 98 (2024). https://doi.org/10.1038/s41526-024-00437-w

G. Pochwatko, Kopec, W., Nilsson, T., et al, Well-being in Isolation: Exploring Artistic Immersive Virtual Environments in a Simulated Lunar Habitat to Alleviate Asthenia Symptoms, 2023 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), Sydney, Australia, 2023, pp. 185-194, doi: 10.1109/ISMAR59233.2023.00033.

Supervisors team

Adam Kuzdralinski, Ph.dr hab. ., Wieslaw Kopeć, Ph.D., Monika Kornacka (Interdisciplinary Laboratory of Emotional Regulation and Cognitive Processes, SWPS University), Tommy Nilsson, Ph.D. (European Space Agency, European Astronaut Centre)

Contact persons

Dr. Wieslaw Kopeć - kopec@pjwstk.edu.pl

Description of the problem

PJAIT 's XR Center conducts research on immersive systems in the context of human performance and human factors research, particularly in space exploration and isolation conditions within Alpha-XR, as well as their applications in real-world settings, including healthcare systems and interventions [1].

The augmented reality (XR) context, including virtual reality (VR), augmented reality (AR) and mixed reality (MR), offers transformative potential for immersive diagnostic and therapeutic environments in healthcare, both in isolation and in space exploration, as well as in daily practice as recommended by the ESA Topic Team on spatial analogs and human performance [2]. These systems enable realistic, interactive patient assessment and intervention scenarios, supporting personalized and data-driven care and prevention. However, realizing this potential requires effective integration of multimodal data and the development of adaptive, user-centered XR experiences based on a participatory approach [3].

Current XR applications in healthcare often rely on limited data sources, ignoring the rich information available from multimodal measurements. This includes self-reported data (e.g., patient questionnaires, symptom diaries), objective physiological data (e.g., EEG, ECG, eye tracking), and behavioral data (e.g., motion recording, voice analysis). In addition, the lack of personalized adaptation in XR systems can lead to suboptimal diagnostic accuracy and treatment effectiveness.

For this transdisciplinary research topic, there are several key research areas related to the study and development of augmented reality (XR) continuity systems for immersive diagnostic and therapeutic environments, including:

1. Development of multimodal data integration frameworks: Design and implementation of a framework for seamless integration and analysis of multimodal data streams in XR environments. This includes developing algorithms for data fusion, feature extraction, and pattern recognition to identify clinically relevant biomarkers and patient conditions.
2. Developing adaptive XR diagnostic and therapeutic protocols: Develop adaptive XR protocols that dynamically adapt to individual patient characteristics and real-time data. This includes using machine learning and artificial intelligence to personalize diagnostic assessments and therapeutic interventions based on multimodal data analysis.
3. Evaluating the Effectiveness of Immersive XR Environments: Conduct research and analysis with direct user participation to evaluate the effectiveness of immersive XR environments in the diagnosis and treatment of specific conditions. This includes evaluating diagnostic accuracy, treatment outcomes, patient engagement and user experience.
4. Exploring the impact of self-reported data and objective measurements: Analyzing the interplay between self-reported data and objective measurements in XR-based diagnosis and treatment. This includes examining how patients' perceptions and experiences correlate with physiological and behavioral data, and how this information can be used to improve personalized interventions.
5. Addressing ethical and privacy issues: Investigating and addressing ethical and privacy issues related to the collection and use of multimodal data in healthcare XR applications. This includes developing guidelines for data security, informed consent and patient autonomy.

The main hypothesis is that the integration of multimodal data and the development of adaptive XR protocols will greatly enhance the accuracy and efficiency of immersive diagnostic and therapeutic environments. Due to its transdisciplinary nature, this research requires a strong understanding of immersive systems and medical knowledge. This research will contribute to the development of personalized and data-driven XR solutions in healthcare, within PJAIT 's XR Center and the Human Aspects in Science and Engineering (HASE) research group. 

Bibliography

Kopeć, W., Pochwatko, G., Kornacka, M., et al.(2024). Human Factors in Space Exploration: Opportunities for International and Interdisciplinary Collaboration. In: Biele, C., et al. Digital Interaction and Machine Intelligence. MIDI 2023. Lecture Notes in Networks and Systems, vol 1076. Springer, Cham. https://doi.org/10.1007/978-3-031-66594-3_35

De la Torre, Kopeć, W. et al. Space Analogs and Behavioral Health Performance Research review and recommendations checklist from ESA Topical Team. npj Microgravity 10, 98 (2024). https://doi.org/10.1038/s41526-024-00437-w

Kopeć, W., Kornacka, M., Nilsson, T. et al. (2023). Co-designing Immersive Virtual and Extended Reality Systems for Remote and Unsupervised Interaction, Intervention, Training and Research. In: Abdelnour Nocera, J., Kristín Lárusdóttir, M., Petrie, H., Piccinno, A., Winckler, M. (eds) Human-Computer Interaction - INTERACT 2023. Lecture Notes in Computer Science, vol 14145. Springer, Cham. https://doi.org/10.1007/978-3-031-42293-5_81

Supervisors team

dr hab. . Adam Kuzdralinski, Dr. Tomasz Ociepa

Contact persons

dr hab. Adam Kuzdralinski - akuzdralinski@pjwstk.edu.pl

Description of the problem

The overarching goal of the project will be to develop a new algorithm for encoding information in DNA, integrating an informatics approach with the biophysicochemical limitations of nucleic acids. The goal is to develop a method for recording data in DNA sequences that demonstrates high resistance to degradation of synthetic genetic material and minimization of reading errors, while taking into account the limitations of synthesis and sequencing technologies.

The research will include a detailed analysis of the factors affecting the chemical stability of DNA, which will allow the design of an algorithm to generate sequences with increased stability. This will be achieved through precise control of nucleotide composition, elimination of unstable sequence motifs, use of advanced double-stranded structures, physical redundancy mechanisms and others.

In parallel, the project will take into account technical parameters of DNA synthesis, such as the maximum length of the obtained fragments, the need for indexing sequences, and strategies for folding fragments into longer sequences. The proposed algorithm will optimize the division of data into fragments while minimizing information overhead (but taking into account the necessary redundancy), while taking into account adaptation to future technologies (such as enzymatic synthesis of long sequences, as well as longer reads during sequencing).

Another important element of the work will be the adaptation of the algorithm to the specifics of various sequencing platforms. It is planned to develop coding schemes compatible with both Illumina sequencers (characterized by short reads and low substitution errors, in data processing requiring fragment folding) and Oxford Nanopore technology (allowing very long reads, but subject to frequent indel errors). Research will include optimization of sequence parameters (GC composition, homopolymers, sites recognized by restriction enzymes, sites particularly susceptible to degradation) and implementation of control patterns to facilitate synchronization of data reads. The algorithm will dynamically adapt to the error profile of a particular sequencing technology.

The fundamental IT aspect of the project will be an advanced error correction system dedicated to the recording of DNA, a macromolecule with specific characteristics. The work will include an analysis of existing correction codes and proposed improvements to them. The implementation of a hierarchical correction system operating at two levels is envisaged: error correction within individual DNA strands and at the level of the entire set of fragments.

The proposed research is interdisciplinary, combining advanced computer science concepts with knowledge from the biological sciences (biophysicochemistry of DNA as a polymer, nucleotide chemistry, sequencing and synthesis technologies).

The expected outcome of the project will be a comprehensive system for encoding data in DNA, demonstrating fault tolerance superior to existing solutions. The results of the research will make a significant contribution to the development of an IT foundation for biological storage.

Tasks

1. Literature review:

- An in-depth analysis of existing DNA data coding methods, with a focus on the challenges of degradation, synthesis and sequencing (Illumina, Oxford Nanopore and other technologies).

- Identify key factors affecting DNA sequence stability (e.g., nucleotide composition, secondary structures, limitations of synthesis technology).

- Review of current error correction strategies used in DNA memory systems and analysis of their effectiveness and limitations.

1. Coding algorithm design:

- Develop a DNA data encoding algorithm that addresses the challenges described above.

- Design a strategy for splitting data into fragments, taking into account oligonucleotide lengths, indexing and possible redundancy, so as to minimize the impact of degradation.

- Include constraints related to maintaining optimal GC content, avoiding long homopolymers and other sequence restrictions affecting synthesis and sequencing processes.

1. Implementation of error correction mechanisms:

- Integrate error correction algorithms that work on two levels: inside individual sequences (correction of nucleotide, indels and other errors) and on a larger scale.

- Experimental pilot simulations of correction algorithms.

1. Evaluating system efficiency and optimization:

- Conduct advanced simulations evaluating transcript density, redundancy overhead and error correction efficiency under various scenarios (varying degradation conditions, different sequencing error profiles, impact of environmental factors).

- Completing the algorithm in the context of changing technological constraints.

- Development of a prototype system to test the developed algorithm.

Bibliography

Kopeć, W., Pochwatko, G., Kornacka, M., et al.(2024). Human Factors in Space Exploration: Opportunities for International and Interdisciplinary Collaboration. In: Biele, C., et al. Digital Interaction and Machine Intelligence. MIDI 2023. Lecture Notes in Networks and Systems, vol 1076. Springer, Cham. https://doi.org/10.1007/978-3-031-66594-3_35

De la Torre, Kopeć, W. et al. Space Analogs and Behavioral Health Performance Research review and recommendations checklist from ESA Topical Team. npj Microgravity 10, 98 (2024). https://doi.org/10.1038/s41526-024-00437-w

G. Pochwatko, Kopec, W., Nilsson, T., et al, Well-being in Isolation: Exploring Artistic Immersive Virtual Environments in a Simulated Lunar Habitat to Alleviate Asthenia Symptoms, 2023 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), Sydney, Australia, 2023, pp. 185-194, doi: 10.1109/ISMAR59233.2023.00033.

Program description

The program focuses on exploring how contemporary art reflects, shapes and challenges various aspects of identity, such as gender, racial, national or sexual identity. Participants will analyze how artists respond to current social and political issues such as migration, human rights, social inequality, post-colonialism or globalization. The program also aims to create their own art projects that enter into dialogue with these issues, as well as to explore the impact of art on the formation of individual and collective identity.

Goals of the program

1. Analysis of how contemporary art reflects and influences perceptions of identity.
2. Create art projects that respond to current social and political challenges.
3. Exploring the role of art in the construction of individual and collective identity in the context of globalization and postcolonialism.
4. Exploring new forms of artistic expression that challenge stereotypes and social norms.

Tasks

1. Analysis of contemporary artworks - a review of works by artists dealing with the theme of identity and its transformation in the context of social problems.
2. Creating art projects - realizing their own works that address issues of identity in light of global challenges such as migration, social crises, gender and racial inequality.
3. Exploring the impact of art on identity - analyzing how art shapes perceptions of self and others in the era of post-colonialism and globalization.
4. Organization of exhibitions and events - presentation of projects in public spaces or galleries to engage the community and stimulate discussion.
5. Publication of research results - documentation of the creative process and research conclusions in the form of articles, exhibition catalogs or multimedia presentations.

Selected research issues

• Representation of gender and sexual identity in contemporary art - an exploration of how artists explore and express issues related to gender or sexuality.
• Migration and identity in art - an exploration of the themes of migration, refugees and displacement in the context of identity construction and redefinition.
• Art as a tool for social activism - exploring how artists use their work to effect social and political change.
• Visual culture and national and global identities - an analysis of how art balances local identities and global cultural narratives.
• The impact of digital media on identity formation - exploring how digital technologies, such as social media and VR, affect the ways in which identity is represented and perceived.

Cooperation in the research group

The program envisions an interdisciplinary collaboration, bringing together artists, sociologists, cultural theorists and activists. This diversity of perspectives will allow for a deeper understanding of identity issues and enable the creation of projects that not only aesthetically, but also socially resonate with current challenges.

Supervisors

dr hab. Anna Barlik - annabarlik@pja.edu.pl, Dr. Magdalena Zdrazil - astromagda@pjwstk.edu.pl

Program description

The program focuses on exploring the role of local cultural, social and geographic contexts in contemporary art. Participants will explore how art responds to the specificity of place - both in a physical and symbolic sense - and how local narratives, traditions and social issues influence artistic creation. An important aspect of the program is also to understand how global processes such as migration, urbanization, climate change and the globalization of culture affect local identities and artistic practices. The program envisions conducting field research in a variety of local settings - from small communities to large urban centers - taking into account their unique historical, social and political contexts. The results of this research will form the basis for creating art projects that enter into dialogue with the studied place and its inhabitants.

Goals of the program

Tasks

1. A review of literature and case studies on the art of place and the role of local context
   in art making around the world.
2. Conducting field research in selected locations - documentation of local
   history,
   traditions and social problems.
3. Analysis of the impact of global phenomena (migration, urbanization, climate change) on place-related art
   .
4. Creating art projects in response to local challenges - site-
   specific works, installations, actions in public spaces.
5. Working with local communities - involving residents in the creative process and
   organizing workshops and art events.
6. Presentation of research results and works in the form of exhibitions, publications or multimedia
   projects.
7. Analyzing the impact of art on local communities - exploring how
   art projects can support identity building and address social issues.

Research topics to choose from

1. Site-specific art and identity of place - an exploration of how artworks fit into
   local context, responding to its history, architecture and community.
2. Local narratives vs. global challenges - an analysis of how local art communities
   are responding to global issues such as climate change, migration and
   social conflicts.
3. Postcolonial Context in Local Art - an exploration of how the legacy of colonialism influences
   
   contemporary art practices in different regions of the world.
4. Art as a tool for community building - analysis of art projects
   engaging
5. local communities in the creative process and social change.
6. Urbanization and Transformation of Public Space - an exploration of how art responds to
   transformation
   urban transformation and gentrification processes.
7. Disappearing traditions and their reinterpretation in contemporary art - documenting and
   artistic
   processing of local cultural traditions that are often in danger of disappearing.

Work methods

1. Field research: trips to selected sites, documentation of spaces, interviews with
2. local communities, audiovisual recording.
3. Interdisciplinary approach: working with sociologists, anthropologists, urban planners and
   local activists.
4. Participatory projects: involving local communities in the creative process, workshops
   and collaborative art activities.
5. Multimedia documentation: creating films, photographs, installations that capture the specifics
   of local contexts.

End results

1. Art projects implemented in selected locations.
2. Scientific and artistic publications documenting the research and creative process.
3. Exhibitions and presentations of research results in galleries and public spaces.
4. Developing innovative methods for exploring and representing local identities in the arts. The program allows participants to take a deep dive into local
   cultural contexts, while offering tools to analyze global phenomena affecting art and identity.

Supervisors

dr hab. Anna Barlik - annabarlik@pja.edu.pl, Dr. Magdalena Zdrazil - astromagda@pjwstk.edu.pl

Description of the problem

The task will include research on issues:

• Photographic observation as a mediated image of reality
• Photography as a source of creation and creation of workshop means of expression
• Photographic image - a code of content and dialogue with the viewer
• Philosophical and social aspect of mediated communication
• Digital photography as a source of recording a fragment of reality and re-evoking information and sensory impressions and emotions. The context of the
  photographic medium in creation and its relationship to the recall of memory, the image of the brief, irreversible and past present; symbolic presence - an introduction to the research issue
• Creative process - artistic praxis based on interdisciplinary activities

Cooperation in the research group 

Doctoral students tackling the described problem will collaborate with the research group of dr hab. Beata Cedrzynska, who collaborates/cooperates with academics at art schools in
within the framework of research projects (Podoblizny, (Un)told - what lies at the heart of photography and memory).

Tasks

1. Review of the literature and current research projects in this area.
2. Analyze the materials and refer to them in the context of your own project.
3. Selection of team members.
4. Create an action plan to initiate the creation process.
5. Taking artistic action.
6. Creation of an artistic work.

Supervisors

Dr hab. Beata Cedrzynska - beata.cedrzynska@pja.edu.pl

Description of the problem

Contemporary art is increasingly combining manual techniques with modern digital technologies, creating new forms of artistic expression that engage audiences in ways that are different than before. The research topic "Hybrid Art" aims to explore creative processes in which traditional manual techniques, such as sculpture, painting, graphic design workshop, drawing, ceramics, for example, will be blended with digital tools - such as image processing programs, 3D technology or augmented reality (AR). An important research question will be - how the combination of these two worlds (manual and digital, generative) affects the perception of the work, its interactivity, and its message in the context of new media. The goal of such a project is to push the boundaries of the traditional understanding of maestria. To search for new ways where art becomes a living, autopsy that can be changeable, interactive or individualized. In this way, the artist not only creates a work, but also proposes new ways of perception, discovery and relationship with the work. This is a new art form that combines technology, creativity and contemporary issues into a coherent, dynamic whole. In this case, it will be important to creatively combine aesthetics and function, where art becomes more than just a form of expression. It becomes integrated with technology in a way that gives it functionality. For example, it can serve a therapeutic, didactic or mobilizing function.

Cooperation in the research group 

The research group would consist of artists of various specialties, multimedia designers, practitioners from new technologies, psychologists and sociologists concerned with the interaction of art with society. The cooperation will include both experimental attempts: artistic, technical and technological, as well as the analysis of final creative processes demonstrating how audiences react to new art that transcends habits and patterns. An important aspect will be the exchange of experience and knowledge about new digital tools, and their experimental mixing.

Tasks

1. Review and analysis of related realizations, literature and current research in this area in the world.
2. Creating an outline of the work and activations initiating the conceptual process.
3. Develop prototypes for in-house research to facilitate the integration of manual art techniques with digital ones.
4. Research process and analysis of results.
5. Final evaluation.
6. Final creation of the work.
7. Publication, exhibition in public space.

Supervisors

Prof. dr hab. . Andrzej Kalina, Dr. Magdalena Zdražil - astromagda@pjwstk.edu.pl

Description of the problem

In the context of the development of artificial intelligence, and generative algorithms, art faces new challenges - can machines create authentic art, or will their
creations remain mechanical and devoid of emotion and feeling? In turn, how can manual techniques be used to complement the AI-based creative process and not lose the value, warmth and beauty of traditional manual arts? The research aims to explore the combination of traditional artistic methods with algorithmic processes for creating paintings, sculptures or art installations. What are the limits of creativity and authenticity when creativity is aided by algorithms and generativity? An important and complementary aspect, will be an exploration of the ethical and philosophical aspects of creativity, the product of which is both artist and machine.

Cooperation in the research group 

The research group will consist of fine artists from various disciplines, AI programmers, art philosophers and specialists in digital media theory. The collaboration aims to develop new creative tools based on artificial intelligence, with an emphasis on the use of traditional and manual techniques. Also to study the impact of AI on creative processes in the context of traditional crafts. The joint work will include the creation of artistic experiments, as well as philosophical analyses on the importance of creativity in the machine age.

Tasks

1. Review and analysis of related realizations, literature and current research in this area in the world.
2. Creating an outline of the work and activations initiating the conceptual process.
3. Development of prototypes for in-house research.
4. Research process and analysis of results.
5. Final evaluation.
6. Final creation of the work.
7. Publication, exhibition in public space.

Supervisors

Prof. dr hab. . Andrzej Kalina, Dr. Magdalena Zdražil - astromagda@pjwstk.edu.pl

Description of the problem

The task will include research on issues:

Color as identification and communication

The emotional and psychological impact of color on the reception of space and product.

The expressive-symbolic value of color in the history of art.

Color in marketing and business.

Color as a symbol and value in the humanist tradition

Theories of color perception - introduction to the research problem. The results of previous research and findings in the field of defining colors and their perception, from the point of view of physics and physiology (structure of the eye, follow-up images, color contrast, the phenomenon of adaptation, constancy of color perception, color vision).

Color as an element of the structure of space, defining the compositional axes, introducing accents, emphasizing differences in scale.

Understand the impact of color, its importance in different cultures, and audience preferences

Cooperation in the research group 

Doctoral students tackling the described problem will work with the research group of dr hab. Agnieszka Ziemiszewska and Dr. Marta Paulat.

Tasks

1. A review of the literature, bibliographies and current projects in this area.
2. Analyze the materials acquired and address them in the context of your project.
3. Outline a plan for your activities/select other team members
4. Undertaking an artistic/project activity.
5. Creation of an artistic/design work.

Supervisors

Dr hab. Agnieszka Ziemiszewska - aziemiszewska@pja.edu.pl, Dr. Marta Paulat - marta.paulat@pja.edu.pl

Description of the problem

The research conducted in this area is based on the recognition and identification of areas of development of cross-cultural and cross-system text communication. It is also important to recognize the direction of development of design tools and methods and the creation of innovative design solutions in this area, such as a tool for text communication in VR (calligraphy - tool writings, as well as typography - prefabricated writing).

Cooperation in the research group 

Doctoral students tackling the described problem will be able to choose between the Future-Text dr hab. research group by Ewa Satalecka, Text and Image dr hab. by Agnieszka Ziemiszewska or Multimedia Communication design dr hab. by Anna Klimczak. Each team undertakes creative work based on existing research results proposes and tests their own design prototypes.

Tasks

1. A review of case studies, literature and current research in this area in the world.
2. Analyze the materials obtained and define the scope of its own activities.
3. Creation of an outline of works and activities that initiate the creative process.
4. Creating a prototype for testing.
5. Research and evaluation.
6. Analysis of results.
7. Creation of an artistic or design work.
8. Publication.

Supervisors

Dr hab. Ewa Satalecka - ewasatalecka@pja.edu.pl, dr hab. Agnieszka Ziemiszewska - aziemiszewska@pja.edu.pl, dr hab. Anna Klimczak - aklimczak@pja.edu.pl, dr hab. Jasmina Wójcik-Wróblewska - jasmninawojcik@pja.edu.pl, dr hab. Jan Piechota - janpiechota@pja.edu.pl

Description of the problem

The task is to identify and define the tool needs of creative teams, and then create proposals and test creative tools to facilitate/accelerate the creative process.

Cooperation in the research group 

Doctoral students tackling the described problem will work in interdisciplinary teams flexibly adapting their activities to the needs of the teams. PhD students tackling the described problem will be able to collaborate with Ewa Satalecka, Ph.D dr hab. dr hab. and Anna Klimczak, Ph.D., and in the area of artistic graphics with Prof. Andrzej Kalina, Ph.D., and lecturers from partner universities involved in AI research.

Tasks

1. A review of case studies, literature and current research in this area in the world.
2. Critical analysis of activities.
3. Creation of an outline of works and activities that initiate the creative process.
4. Creating a prototype for testing.
5. Research and evaluation.
6. Analysis of results.
7. Creation of an artistic or design work.
8. Publication.

Supervisors

Dr hab. Ewa Satalecka - ewasatalecka@pja.edu.pl, dr hab. Anna Klimczak - aklimczak@pja.edu.pl, Prof. Andrzej Kalina - ziemekart@pja.edu.pl, dr hab. dr hab. Jan Piechota - janpiechota@pja.edu.pl, dr hab. . Joanna Polak - jpolak@pja.edu.pl

Description of the problem

How to use tools from art, design and visual language to initiate long-term social change. Research issues based on art interventions related to practices in: reciprocity, caring, care, shared responsibility, working with community(s), activism. Projects combining tools from different fields - tkj art, design, graphic design, sociology, philosophy, gender studies, childhood studies (possible all others according to the interests of the doctoral student/artist). Activities aimed at interdisciplinarity, combining different tools (patchwork, teamwork, assuming exchange of experiences, sentences and scopes of research fields).

(feminism, social design, care, socially engaged film, long term process artistic or socio-artistic projects, motherhood, ecology, uprecycling, coworking with communities)

Cooperation in the research group 

Doctoral students tackling the described problem will be able to choose between the Future-Text research group dr hab. by Ewa Satalecka, Text and Image dr hab. by Agnieszka Ziemiszewska or Multimedia Communication design dr hab. by Anna Klimczak.

Each team undertakes creative work based on existing research results proposes and tests its own design prototypes.

Tasks

1. review of case studies, bibliographies and current projects in the field

2. analyze the materials obtained and refer to them from the scope of their own activities (and capabilities)

3. outline the starting point, select other members/ins of the working/project team, identify their skills/gaps/resources

4. working with the group of people with whom the activities will be carried out (listening to them, establishing a relationship, or deepening it)

5. identify common goals, gaps, topics for negotiation

6. undertake a design activity together in a participatory process with the selected community (in the realm of prototyping, inventing, visual imagining, exercising imagination)

7. the creation of an artistic or design work with the inclusion of the community (to the extent established in advance)

8. realization of the action as a seed, initiation and critical learning for its continuation (with possible change of thinking, change of design tools, change of mindset).

Supervisors

Dr hab. Ewa Satalecka - ewasatalecka@pja.edu.pl, dr hab. Agnieszka Ziemiszewska - aziemiszewska@pja.edu.pl, dr hab. Anna Klimczak - aklimczak@pja.edu.pl, dr hab. Jasmina Wójcik-Wróblewska - jasmninawojcik@pja.edu.pl, dr hab. Jan Piechota - janpiechota@pja.edu.pl