Selected work

Representative Papers

Designing a Token Economy: Incentives, Governance, and Tokenomics

Feb 10, 2026

This study addresses the absence of a systematic, reusable, and empirically grounded end-to-end approach that integrates incentive mechanisms, governance structures, and tokenomics in current token economic designs. To bridge this gap, the paper proposes the Token Economic Design Method (TEDM), which, for the first time, unifies these three dimensions into a structured and actionable design framework, with explicit emphasis on sociotechnical context and early-stage design considerations. Developed through the design science research paradigm and informed by qualitative synthesis, co-design case studies, and expert interviews, TEDM was empirically validated through its application to the Currynomics stablecoin ecosystem and subsequent expert evaluation. The results demonstrate that TEDM effectively supports the analysis and construction of tokenized ecosystems, offering practical and reusable design guidance.

3 citationsRead paper

Spook the Machine: Gamified Exploration of Human Imagination of Machine Fear

Sep 14, 2026

研究通过Spook the Machine平台，利用情感表达和奖励机制设计，探索人类如何与表达恐惧的AI互动，影响用户参与度及创意多样性。

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Student Evaluation of Repeated AI Feedback Across a Semester of Writing

Jul 17, 2026

This study addresses the paucity of empirical research on the longitudinal, repeated use of generative AI for writing feedback in higher education, particularly regarding students’ perceived effectiveness and learning impacts. Drawing on 2,988 reflective essays from 283 Estonian undergraduates over one semester, the research integrates student-selected AI-generated feedback—elicited via a standardized prompt—and students’ evaluations thereof. Employing a mixed-methods approach combining manual content analysis and a validated AI text classifier, this work provides the first large-scale, longitudinal classroom evidence of evolving student attitudes toward AI feedback: while most students initially found it helpful and actionable, approximately 10% developed negative perceptions over time. Findings underscore that although AI offers rapid suggestions, its benefits depend on students’ critical and selective engagement to avoid overreliance, which may otherwise compromise reflective depth and learning outcomes.

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Modeling Engagement with Brand and Organizational TikTok Videos Using Machine-Assisted Theory-Ensemble Annotation

Jun 14, 2026

Short-form videos pose significant challenges for standardized modeling of user engagement due to their multimodal content and platform-specific algorithms. This study addresses this gap by computationally operationalizing classical interpretive theories from narratology, rhetoric, communication studies, and semiotics at scale. Leveraging a multimodal large language model, we automatically annotated 77 theory-driven structural variables across approximately 10,000 TikTok videos from Estonian brands and institutions, supplemented by human validation to assess reliability. Controlling for account size and video age, our model yielded a stable, albeit modest, improvement in predicting user engagement. Results indicate that variables related to perception and communication were reliably annotated, whereas deeper semiotic and archetypal structures proved more challenging to capture. This work establishes a systematic computational framework for analyzing the cultural structures embedded in short-form video content.

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How Much Trust is Enough? Towards Calibrating Trust in Technology

Apr 07, 2026

This study addresses the trust imbalance that arises when users interact with increasingly prevalent yet opaque autonomous systems, often due to an inadequate understanding of their capabilities and limitations. Building upon the Human-Computer Trust Scale (HCTS), this work proposes the first practice-oriented, context-sensitive explanatory framework that enables reflective interpretation of trust dispositions. Through empirical validation, the research not only confirms the efficacy of HCTS as an initial trust assessment instrument but also introduces context-aware calibration guidelines for aligning user trust with system performance. The resulting framework provides both theoretical grounding and practical support for dynamically regulating trust in human–computer interaction.

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Recent publications

Latest Papers

Spook the Machine: Gamified Exploration of Human Imagination of Machine Fear

Sep 14, 2026

研究通过Spook the Machine平台，利用情感表达和奖励机制设计，探索人类如何与表达恐惧的AI互动，影响用户参与度及创意多样性。

0 citationsRead paper

Student Evaluation of Repeated AI Feedback Across a Semester of Writing

Jul 17, 2026

This study addresses the paucity of empirical research on the longitudinal, repeated use of generative AI for writing feedback in higher education, particularly regarding students’ perceived effectiveness and learning impacts. Drawing on 2,988 reflective essays from 283 Estonian undergraduates over one semester, the research integrates student-selected AI-generated feedback—elicited via a standardized prompt—and students’ evaluations thereof. Employing a mixed-methods approach combining manual content analysis and a validated AI text classifier, this work provides the first large-scale, longitudinal classroom evidence of evolving student attitudes toward AI feedback: while most students initially found it helpful and actionable, approximately 10% developed negative perceptions over time. Findings underscore that although AI offers rapid suggestions, its benefits depend on students’ critical and selective engagement to avoid overreliance, which may otherwise compromise reflective depth and learning outcomes.

0 citationsRead paper

Modeling Engagement with Brand and Organizational TikTok Videos Using Machine-Assisted Theory-Ensemble Annotation

Jun 14, 2026

Short-form videos pose significant challenges for standardized modeling of user engagement due to their multimodal content and platform-specific algorithms. This study addresses this gap by computationally operationalizing classical interpretive theories from narratology, rhetoric, communication studies, and semiotics at scale. Leveraging a multimodal large language model, we automatically annotated 77 theory-driven structural variables across approximately 10,000 TikTok videos from Estonian brands and institutions, supplemented by human validation to assess reliability. Controlling for account size and video age, our model yielded a stable, albeit modest, improvement in predicting user engagement. Results indicate that variables related to perception and communication were reliably annotated, whereas deeper semiotic and archetypal structures proved more challenging to capture. This work establishes a systematic computational framework for analyzing the cultural structures embedded in short-form video content.

0 citationsRead paper

How Much Trust is Enough? Towards Calibrating Trust in Technology

Apr 07, 2026

This study addresses the trust imbalance that arises when users interact with increasingly prevalent yet opaque autonomous systems, often due to an inadequate understanding of their capabilities and limitations. Building upon the Human-Computer Trust Scale (HCTS), this work proposes the first practice-oriented, context-sensitive explanatory framework that enables reflective interpretation of trust dispositions. Through empirical validation, the research not only confirms the efficacy of HCTS as an initial trust assessment instrument but also introduces context-aware calibration guidelines for aligning user trust with system performance. The resulting framework provides both theoretical grounding and practical support for dynamically regulating trust in human–computer interaction.

0 citationsRead paper

EstLLM: Enhancing Estonian Capabilities in Multilingual LLMs via Continued Pretraining and Post-Training

Mar 02, 2026

This work addresses the significant performance gap of multilingual large language models on low-resource languages such as Estonian, while maintaining strong capabilities in high-resource languages and general tasks. Building upon Llama 3.1 8B, the authors propose a balanced multilingual data mixing strategy for continued pretraining, augmented with English replay and enriched with code, mathematical, and instructional data. The model is further aligned through supervised fine-tuning, preference optimization, and chat vector fusion techniques. This approach yields substantial improvements across Estonian language understanding, knowledge recall, reasoning, translation, and instruction-following benchmarks, while preserving competitive performance on English and general-purpose evaluations, thereby achieving an effective balance between low-resource language enhancement and overall multilingual competence.

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