AI-Driven Engine for Context-Aware Digital Competences Assessment

Revolutionizing training: Our AI project uses LLMs to add context to Situational Judgment Tests, enhancing digital competency assessments for the workforce


This research project aims to address the challenges posed by the rapid digitalization of the workforce, which demands extensive re- and up-skilling across the globe. Recognizing the limitations of current competency frameworks, which often overlook the nuanced scenarios professionals encounter, the project proposes the development of an AI-driven engine for digital competencies. This engine, powered by large language models, seeks to enhance situational judgment tests (SJTs) by generating realistic, context-specific work scenarios. The innovative, dialog-based assessment method integrates personal context, offering a tailored evaluation of digital competencies. This approach not only fills a crucial gap in current educational and professional training programs by improving the relevance and accuracy of competency assessments but also ensures inclusivity and personalization in competency evaluation, paving the way for a more adaptable and skilled future workforce.

(Zwischen-) Ergebnisse und Infos zum Projektstand

Our research questions are:


RQ1: What specific contextual variables within diverse organizational settings demonstrate a measurable correlation with the accuracy and comprehensiveness of capturing competency levels in digital competency assessments with situational judgment tests?


RQ2: How can large language models be employed to systematically augment existing competency frameworks, facilitating more context-aware, human-centric competence assessments?


The project aims to develop a versatile and adaptable measurement instrument for assessing digital competencies through the Situational Judgement Test (SJT) methodology. Designed for a wide range of settings, from educational institutions to corporate environments, the instrument emphasizes modularity and customization to ensure its long-term relevance and utility. To further its applicability and sustainability, strategies such as offering a ‹white-label› version, partnering for specialized module development, and providing tailored tools and workshops are being explored. Supported by the Institute for Digital Technology Management at BFH, this initiative seeks to create a sustainable resource for effectively measuring and enhancing digital literacy and skills.


Beteiligte Personen

Beteiligte Institutionen