Contemporary societies are characterised by increasing volatility and rapid transformation, in which “the ability to learn has never been as important as it is today” (UNESCO, 2015, p. 41). This claim was empirically reinforced by the COVID-19 pandemic (2020–2022), which exposed the need for education systems to respond flexibly to rapidly changing political, economic, and social conditions. The abrupt transition to remote learning revealed both structural vulnerabilities and pedagogical challenges, highlighting the importance of research that connects educational knowledge with informed action. This orientation directly aligns with the EERA ECER 2026 conference theme Knowing and Acting: The Changing Conditions and Potentials of Education Research.

Empirical studies demonstrate that school closures and distance learning negatively affected students’ learning outcomes (Maldonado & De Witte, 2021; König & Frey, 2022), with particularly pronounced learning gaps in mathematics (Aguhayon et al., 2023) and native language (L1) education (Westhoff et al., 2023). In the domain of native language learning, the most frequently reported gaps concern written text comprehension (Westhoff et al., 2023). Linguistic research consistently shows that limited vocabulary, reduced semantic flexibility, and insufficient morphological awareness restrict students’ ability to construct meaning from texts, making these factors key predictors of comprehension difficulties (James et al., 2023; Rueda-Sánchez et al., 2025).

Similarly, research in mathematics education indicates that following school closures teachers observed substantial deficiencies in students’ understanding of arithmetic operations with whole numbers—addition, subtraction, multiplication, and division—as well as weakened conceptual knowledge of numerical operations (Baiño, 2025). These mathematical gaps were often compounded by students’ difficulties in reading comprehension, which hindered their ability to solve word-based tasks. In addition to cognitive factors, researchers highlight affective and motivational dimensions, noting that learning gaps are reinforced by reduced engagement, negative learning attitudes, and diminished motivation (Westhoff et al., 2023; Aguhayon et al., 2023).

Evidence from Lithuania reflects similar patterns. A national study identified major negative aspects of distance learning among Lithuanian schoolchildren, including reduced socialisation, difficulties with concentration and sustained attention, excessive independent workload, and lack of motivation to learn (Gaidelys et al., 2022). These findings motivated the development of the project Edukologijos proveržis (Breakthrough in Education), which aims to design an artificial intelligence–based tool to support the transition from remote learning and mitigate its long-term consequences. The proposed tool seeks not only to address existing learning gaps but also to strengthen students’ self-directed learning competencies.

Research Question.
How do teachers identify, interpret, and prioritise students’ learning gaps in native language (L1) and mathematics in relation to curriculum expectations?

Aim and Objectives.
The primary aim of the study is to examine teachers’ perspectives on learning gaps in native language (L1) and mathematics. The objectives are: (1) to analyse general education curricula to identify core learning areas and topics; (2) to explore teachers’ views on prevailing learning gaps; and (3) to develop a model of student learning gaps that will serve as the foundation for an artificial intelligence–based programme.

Theoretical Rationale.
Learning gaps are defined as discrepancies between students’ observed achievement and curriculum-based competency requirements (Baiño, 2025; Cabral-Gouveia et al., 2023; Minami & Ogawa, 2025). The development of the AI tool is grounded in self-regulated learning (SRL) theory, which conceptualises learners as active agents capable of planning, monitoring, regulating, and evaluating their learning, emphasising the role of metacognitive competencies in sustainable educational improvement (Panadero et al., 2025; Sun et al., 2025; Aydan, 2025; Chen, 2025; Teng, 2025).

The study commenced in November 2025 and employs a multi-stage mixed methods research design, specifically a sequential explanatory design, in which quantitative data collection and analysis precede and inform qualitative inquiry (Creswell & Creswell, 2024). This approach is widely used in educational research to identify general patterns through quantitative methods and subsequently explain and contextualise these patterns using qualitative data. The design is particularly appropriate for investigating learning gaps, as it allows both the identification of their prevalence and a deeper understanding of their underlying causes. In the first stage, a qualitative content analysis of updated national general education curricula was conducted to identify core learning areas and thematic structures in Lithuanian language (L1) and mathematics across Grades 4, 7–8, and 10. These grade levels were selected as key transitional stages in compulsory and upper-secondary education, where cumulative learning gaps tend to become more visible. The curriculum analysis provided an analytical framework for the development of subsequent research instruments and ensured alignment between empirical findings and officially defined competency expectations. The second stage consisted of a quantitative online survey of teachers (n = 284), designed to identify curriculum domains and specific topics in which students demonstrate learning gaps. A self-selected sampling strategy was applied, with participation based on voluntary informed consent. Although this approach may limit generalisability, it is suitable for exploratory diagnostic research, as it enables the collection of insights from practitioners with direct classroom experience. The survey captured teachers’ perceptions of both the prevalence and severity of learning gaps. Quantitative data were analysed using descriptive statistical methods in SPSS version 28, including frequencies, percentages, means, and standard deviations. The results informed the sampling strategy and thematic focus of the qualitative phase. The third stage involves focus group discussions with teachers (planned n = 30), applying a criterion-based expert selection approach. Inclusion criteria include active teaching in the selected grade levels, at least five years of professional experience, and voluntary participation. Qualitative data are analysed using deductive thematic content analysis in MAXQDA, guided by curriculum structures and quantitative findings. Data synthesis follows an interpretative paradigm, integrating quantitative trends with qualitative interpretations to produce a coherent understanding of student learning gaps.

Based on the aims, objectives, and theoretical foundations of the study, the findings are expected to provide a systematic and empirically grounded understanding of learning gaps in students’ native language (L1) and mathematics within the context of post-pandemic European education. Drawing on curriculum analysis, teachers’ survey data, and qualitative interpretations, the study is expected to identify recurring and structurally embedded learning gaps that emerge at key educational stages (Grades 4, 7–8, and 10), where cumulative learning difficulties become most visible. The identified gaps are anticipated to reflect both cognitive dimensions, such as difficulties in text comprehension, vocabulary development, arithmetic fluency, and conceptual understanding, and motivational and self-regulatory challenges, including reduced engagement and limited monitoring of learning progress. These patterns are expected to align with recent European research on learning recovery and educational inequality. In line with the study’s objectives, the integration of quantitative and qualitative findings will enable the development of evidence-based models for identifying learning gaps in Lithuanian (L1) and mathematics. These models are expected to systematically link curriculum-defined competency requirements with teachers’ empirically observed student performance, operationalising the concept of the learning gap as defined in the theoretical framework. Grounded in self-regulated learning (SRL) theory, the findings are also expected to highlight the role of metacognitive competencies in addressing persistent learning gaps. At a practical level, the study will provide a foundation for developing an artificial intelligence–based diagnostic and self-directed learning tool, supporting personalised learning pathways. From a European perspective, the findings contribute to discussions on learning recovery, educational quality, equity, and digital innovation, offering transferable insights for evidence-informed educational improvement.

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