Abstract
Programming courses in distance learning institutions face two unresolved problems within existing Learning Management Systems. First, students frequently fail to initiate meaningful programming practice due to technical barriers — installation failures, connectivity issues, and device incompatibilities — that exhaust cognitive resources before algorithmic learning can begin. Second, automated assessment in standard Moodle–VPL configurations evaluates only functional correctness through test-case matching, leaving conceptual misalignments undetected: structurally flawed submissions may pass all test cases, while syntactically minor errors receive no diagnostic feedback. To address both gaps, this study developed and validated an AI-Enhanced Python Virtual Laboratory (AI-PVL) embedding GPT-4 semantic assessment within institutional Moodle–VPL infrastructure, enabling code analysis beyond binary test-case matching. A Participatory Design Research (PDR) methodology was employed across five phases with six lecturers at Universitas Terbuka, Indonesia's national open distance university. Needs analysis (N = 65 students) using Importance–Performance Analysis identified five priority features with gap scores exceeding −2.0, confirming that the primary barrier was the absence of foundational environment affordances. The co-design process yielded three unanticipated innovations: a dual-feedback interface, an instructor-facing misconception frequency map, and co-authored semantic prompt templates. Expert validation via Aiken's V confirmed content and media validity at 84–90% feasibility. An initial proof-of-concept deployment produced observable patterns in student assignment progression across three cycles. The AI-PVL is established as technically feasible and institutionally validated; whether it produces measurable learning gains requires a future adequately powered study.
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APA 7th edition
In-text citation: (Noviyanti et al., 2026)
Reference: Noviyanti, M., Sudirman, S., Kandaga, T., Nurhayati, S., & Isnawan, M. G. (2026). AI-enhanced python virtual laboratory integrated with moodle LMS: A qualitative proof-of-concept for computational thinking support in distance education.
European Journal of STEM Education, 11(1), Article 42.
https://doi.org/10.20897/ejsteme/18921
AMA 10th edition
In-text citation: (1), (2), (3), etc.
Reference: Noviyanti M, Sudirman S, Kandaga T, Nurhayati S, Isnawan MG. AI-enhanced python virtual laboratory integrated with moodle LMS: A qualitative proof-of-concept for computational thinking support in distance education.
European Journal of STEM Education. 2026;11(1), 42.
https://doi.org/10.20897/ejsteme/18921
Chicago
In-text citation: (Noviyanti et al., 2026)
Reference: Noviyanti, Mery, Sudirman Sudirman, Thesa Kandaga, Suci Nurhayati, and Muhamad Galang Isnawan. "AI-enhanced python virtual laboratory integrated with moodle LMS: A qualitative proof-of-concept for computational thinking support in distance education".
European Journal of STEM Education 2026 11 no. 1 (2026): 42.
https://doi.org/10.20897/ejsteme/18921
Harvard
In-text citation: (Noviyanti et al., 2026)
Reference: Noviyanti, M., Sudirman, S., Kandaga, T., Nurhayati, S., and Isnawan, M. G. (2026). AI-enhanced python virtual laboratory integrated with moodle LMS: A qualitative proof-of-concept for computational thinking support in distance education.
European Journal of STEM Education, 11(1), 42.
https://doi.org/10.20897/ejsteme/18921
MLA
In-text citation: (Noviyanti et al., 2026)
Reference: Noviyanti, Mery et al. "AI-enhanced python virtual laboratory integrated with moodle LMS: A qualitative proof-of-concept for computational thinking support in distance education".
European Journal of STEM Education, vol. 11, no. 1, 2026, 42.
https://doi.org/10.20897/ejsteme/18921
Vancouver
In-text citation: (1), (2), (3), etc.
Reference: Noviyanti M, Sudirman S, Kandaga T, Nurhayati S, Isnawan MG. AI-enhanced python virtual laboratory integrated with moodle LMS: A qualitative proof-of-concept for computational thinking support in distance education. European Journal of STEM Education. 2026;11(1):42.
https://doi.org/10.20897/ejsteme/18921