Contact: Nuno Correia (nuno@tlu.ee)

Position: Junior Researcher

Topic: Data Visualization and Artificial Intelligence for Monitoring Competence Needs in Industry and Lifelong Learning

The research aim is to investigate how to visualize data related to monitoring competence needs in industry and lifelong learning in combination with Artificial Intelligence (AI).

The Junior Researcher should design and develop effective data visualisations and related dashboard design, also exploring possible roles for the use of AI to enhance such solutions, involving relevant stakeholders.

The data is provided by other components of the Transform project, and harvested from innovations, patents and practical solutions, as well as employment statistics and job postings.

Contact: Kairit Tammets (kairit.tammets@tlu.ee)

Position: Junior Researcher

Topic: AI-Enhanced Learning Processes in Workplace and Professional Development

The PhD project focuses on the intersection of workplace learning, knowledge transfer, and collaborative learning in industry-academia partnerships. This position is part of two research projects that explore how learning processes evolve in professional contexts, with a specific focus on self-directed learning and the integration of AI in shaping professionals’ skills, knowledge, and attitudes.

The research under this topic could possibly Investigate how professionals and trainers engage in workplace learning, knowledge transfer, and collaborative learning through a self-directed learning lens in industry-academy partnerships. Additionally, research could explore how AI technologies impact the development of skills, knowledge, and attitudes in workplace learning processes. This project emphasizes the evolving role of trainers and professionals in AI-enhanced learning environments. The focus is on understanding the interplay between human and AI capabilities rather than the technical aspects of AI development.

We are looking for a candidate who has a technical profile and experience working with AI tools, Is aware of the most common learning theories in the field of self-directed learning, and has strong skills in quantitative and/or mixed-methods research

Contact: Pia Tikka (piatikka@tlu.ee)

Position: Doctoral Student

Topic: Computational phenomenology of first-person experiences

The research conducted under this topic addresses dynamical interactions between 1) verbal first-person descriptions of lived experience, 2) their physiological instantiations, and 3) annotations of media contents and their contexts within which the experiences are embedded.

The methods of data acquisition may be adopted from empirical phenomenology, psychology, humanities and life sciences, besides various means of temporal content and context analysis.

Corresponding analytical and modelling methods may draw from enactive cognitive science, affective computing, and data science, with integrative approaches like computational phenomenology at the core.

A central challenge of the project is to coordinate the methodological repertoire and develop a solidifying epistemology of knowledge-acquisition, ensuring a high-level compatibility over the heterogeneous multi-source data.

Applicants who master at least one of the involved methodologies and are willing to learn new are prioritised.

Contact: Mart Laanpere (mart.laanpere@tlu.ee)

Position: Doctoral Student

Topic: Integrating the Elements of AI in the School Informatics Curriculum

Teaching about how AI works is only briefly introduced in the Estonian national informatics curriculum; there is a need for a more extensive and deeper way to address it in schools.

Contact: Hans Põldoja (hans.poldoja@tlu.ee)

Position: Doctoral Student

Topic: Computational Adopting Version Control Systems in Computer Science Education

This research investigates the challenges of integrating version control systems (VCSs) into computer science study programmes in higher education.

The study aims to (1) identify the barriers which prevent educators from adopting VCS, (2) design and evaluate pedagogical and technological strategies to facilitate VCS adoption, (3) analyze the impact of educator-focused interventions on student engagement and competency with VCS tools, and (4) examine the role of institutional policies and curriculum-level initiatives in shaping VCS integration.

Contact: Sonia Sousa (sonia.sousa@tlu.ee)

Position: Doctoral Student

Topic: Designing Trustworthy User Experiences with Generative Artificial Intelligence

Technology has always been a tool to extend human capabilities. The rise of Artificial Intelligence (AI) and Large Language Models (LLMs), like ChatGPT, marks a transformative shift. Unlike traditional tools, ChatGPT interacts in natural language and generates convincing outputs across various domains based on user prompts. Understanding how to use these tools is crucial for effective interaction with LLMs. However, this has raised concerns in education and research fields. The impact of Generative AI (GenAI) on people's behaviours is not well understood in human-computer interaction (HCI) and computer and education science research. Understanding this from an HCI perspective is vital for improving work and education-related tasks and processes.

This call highlights three research challenges:

1. Design Mechanisms: We need to study new design methods and approaches to help users overcome possible risk-taking or adverse user experience effects of using GenAI tools in various decision-making contexts.
2. User Experience: We need to develop new user experience evaluation methods to understand how socio, personal and technical factors (e.g. digital literacy, cognitive overload, regulations, etc.) impact users' use of GenAI tools and task performance over time.
3. Theoretical insights: We need to enhance current HCI literature with theoretical insights on how specific GenAI tools impact user decisions and support user engagement.

Contact: Abiodun Ogunyemi (abiodun_afolayan.ogunyemi@tlu.ee)

Position: Doctoral Student

Topic: Conversational Pipeline for Eliciting Hedonic Experience Requirements

In product and service design, addressing and managing hedonic experiences—the emotional and psychological responses users encounter during interaction—poses a significant challenge. Despite the increasing recognition of user experience (UX) as a crucial element in successful design, current design processes often prioritize functional and usability requirements, overlooking the hedonic aspects influencing emotions such as joy, excitement, or frustration. These emotional responses significantly impact user satisfaction and brand loyalty, yet they are often underexplored and challenging to integrate into the design phase. Conventional design methods rely on tools such as interviews, surveys, and user testing, which primarily cater to functional needs rather than emotional responses. Emotions' subjective and dynamic nature complicates the process, as users' feelings towards a product or service can evolve. Furthermore, current design practices often lack formalized methods to capture and address these emotional responses systematically and do not incorporate continuous feedback mechanisms to adapt to changing user expectations.

A crucial gap in research and practice lies in developing robust frameworks and tools for capturing hedonic experiences early in product and service design. This necessitates innovative elicitation techniques focusing on users' emotional experiences—such as emotion-oriented personas, experience mapping, and experience prototyping—and leveraging advanced technologies like affective computing and sentiment analysis. Integrating these emotional requirements with iterative, Agile design methodologies, where timelines are compressed, remains a significant challenge. The main objective of this research is to develop and define a repeatable conversational pipeline for eliciting hedonic user experience requirements in software products and service delivery. The pipeline will enable software products and service providers to systematically define repeated experiential increments to transmit hedonic experience requirements into a reusable elicitation pipeline.

Consequently, this research aims to explore and develop formalized frameworks and tools to effectively capture and mitigate hedonic occurrences within the RE paradigm for product and service design. This is essential to ensure software systems and services meet functional requirements and deliver emotionally satisfying experiences. Addressing this gap is critical to advancing RE practices and improving overall user satisfaction in modern software development projects, particularly e-commerce services.

Contact: James Quaicoe (james_sunney.quaicoe@tlu.ee)

Position: Doctoral Student

Topic: Teacher Professional Development and Digital Wellbeing for Digital Transformation in Education

Teacher professional development (TPD) for innovation in teaching and learning continues to remain in some global communities. Existing TPDs, though helpful, could disregard the local cultural and digital status communities’ varied needs in the areas of teacher ICT Competence, local cultural circumstances, the extent of prevailing digital disparities across schools, teacher personal dispositions and digital well-being, among other unknown needs. Resultantly, such challenges give rise to incoherent strategies that overlook digital inequality between rural and urban areas schools, teachers’ digital fears and the lack of support to become professionally competent as change agents. In some communities, teachers’ potential to meet changing educational needs in contemporary classrooms/schools are limited (Quaicoe et al., 2016). In this light, the research seeks to focus on the teacher’s professional development for innovative teaching from the angle of teacher personal dispositions, institutional conditions, digital readiness and well-being, and support systems for a human-centred digital transformation in education(Sarantou & Miettinen, 2022); and in additions empower teachers as the owners of their professional directions in the dynamic influence of digital and resources in the education industry (Quaicoe & Pata, 2020).

Key objectives of the research are as follows:

1. Seek deeper insight into the TPD for professional practices underpinned by innovative digital tools and resources, especially in underserved schools, communities, and regions.
2. Explore teachers’ digital competence, well-being concerns, practices, and local and cultural perspectives within working environments.
3. Develop particular TPD frameworks that address teachers’ personal capacity development needs, cultural dispositions, well-being, and prevailing workplace conditions for school innovation.
4. Research into frameworks to support professional development, digital well-being, technology acceptance, and resilience against workplace intimidations for innovative practices.