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Accelerating digitalization actualizes the importance of secondary science education in developing students’ digital abilities, such as retrieving and appraising scientific information online. However, there is little research and policy guidance on the purposes and challenges of teaching digital abilities in science classrooms. This article reports on a mixed-methods study that explored how upper secondary school students in Sweden reasoned about their digital abilities when searching for scientific information online. The results reveal three key themes: the role of scientific terminology in improving search accuracy, as students noted how precise terms yielded more credible results; the need for deliberate search strategies, including using questions or keywords depending on the context; and, the impact of selective exposure, as students became aware of how personalized results could reinforce existing beliefs and limit perspective diversity. Based on these insights, we introduce the concept of ‘digital versatility’ and outline a model, Digital Versatility in Online Searching (DVOS), that integrates essential digital abilities (source literacy, information abilities, and search abilities) with scientific subject knowledge. The DVOS model provides guidance for teachers in instructional planning and offers a basis for future research on digital abilities in science education.

Digital search strategies are essential for science learning in today’s digital age, but knowledge about students’ search strategies, especially in relation to scientific content, is limited. This study examines how upper-secondary students incorporate scientific content into their search processes. Video recordings of students’ online searches and peer-to-peer discussions during two tasks of varying complexity were analyzed using Legitimation Code Theory (LCT) and semantic analysis. The findings, based on a semantic analysis of students’ reflections, confirmed three previously identified search strategies - linear, one-shot, and random search - while revealing three additional ones: targeted refinement, text-to-image searches, and piecewise searching. These strategies illustrate how students engage with scientific content in different phases of digital search processes. The results indicate that strategies involving conceptual abstraction and iterative refinement were associated with stronger semantic shifts. By adapting LCT to analyze students’ meaning-making the study contributes to a novel framework for exploring how digital search practices intersect with scientific understanding.

Abstract: Digital information searching is a central part of secondary school students’ academic work, yet it is often treated as a set of general technical skills rather than as a subject-specific and strategic ability. This study examines how students describe digital abilities—subject competence, source literacy, information competence, and search competence—in relation to science content and different phases of online search processes. Two research questions are addressed: how students articulate digital abilities, and how these abilities interact across the phases of the search process.

The study was conducted with 27 upper secondary students. Data were collected through logbooks, questionnaires, and pod discussions, and analysed using four activity domains (interpretation, planning, navigation, evaluation) and three knowledge forms (conceptual, procedural, and metacognitive). Findings show that students’ articulation of their digital abilities varied in complexity—from general use of subject terms to strategic adaptation and metacognitive reflection.

Multiple abilities were often activated concurrently, particularly when navigating and evaluating information. The study refines the DVOS (Digital Versatility in Online Searching) model by integrating knowledge forms as an analytical lens, thereby clarifying how digital versatility takes shape in practice. It also contributes new frameworks for understanding and supporting digital versatility in science education.

Denna studie undersöker hur gymnasieelever resonerar kring användningen av generativ AI, särskilt ChatGPT, i samband med digital informationssökning inom naturvetenskap. Syftet är att analysera hur olika aspekter av digitalt kunnande kommer till uttryck i elevernas resonemang. Studien utgår från modellen Digital Versatility in Online Searching (DVOS) och prövar dess relevans i en AI-baserad kontext där informationssökning sker genom dialog snarare än genom traditionella sökmotorer.

Empirin består av klassrumsnära data insamlade 2023 och 2024 genom videodokumenterade elevdiskussioner, enkäter, loggböcker, podcastsamtal och fokusgrupper. Analysen, inspirerad av DVOS-modellens fyra delförmågor – ämneskompetens, källiteracitet, informationskompetens och sökkompetens – fokuserar på hur elevernas resonemang varierar från begreppslig orientering till metakognitiv reflektion. Eleverna beskriver att de använder ChatGPT både som snabb informationskälla och som stöd för att pröva begrepp och resonemang, men deras förhållningssätt till generativ AI spänner från instrumentellt till reflekterande. Många beskrev att de bedömde AI-svar utifrån språkets trovärdighet snarare än ämnesmässig logik, samtidigt som flera uttryckte medvetenhet om teknikens funktion, begränsningar och det egna ansvaret i användningen. Resultaten tyder på att digitalt kunnande i AI-miljöer tar form som en växling mellan språk, teknik och ämnesförståelse, vilket motiverar en anpassning av DVOS till en AI-kontext. Modellen Digital Versatility in AI (DVAI) presenteras som begreppslig modell för att resonera kring hur elever utvecklar ämneskompetens, källkritisk vaksamhet, tekniklogik och dialogisk sökförmåga i mötet med generativ AI.