Executive functions (EF)—including planning/organization, task initiation, working-memory management, cognitive flexibility, and help-seeking regulation—are foundational to engineering education and core software engineering practices. Yet little is known about how learning management systems (LMSs) support students who experience EF challenges in day-to-day coursework. We conducted an exploratory qualitative study using semi-structured interviews with seven engineering students both neurodivergent (ND) and neurotypical—who self-reported EF difficulties, and analyzed the data thematically through a self-regulated learning (SRL) and EF lens. Participants reported recurring EF-linked difficulties: (i) unclear or context-poor instructions prompting repeated re-reading and searches for external examples; (ii) a strong preference for brief visual/worked examples over dense text; (iii) peer-first help-seeking outside the LMS and limited use of discussion boards perceived as inconvenient; (iv) calendars valued and checklists helpful when used consistently (but usage was uneven); (v) email as the primary yet slow channel, with unease about formal messaging; (vi) growing reliance on AI for instant feedback and community sites (e.g., Stack Overflow) for coding questions. Overall, students regarded LMS tools as necessary for organization but insufficient for timely, low-effort clarification and support, leading to compensatory practices beyond the platform and increased cross-system switching. We synthesize an EF–SRL–LMS alignment that locates where EF load accumulates and derive a practice-oriented checklist. We argue for a more personalized, automated layer of in-platform support—adaptive, context-aware, and designed to reduce switching between multiple tools and platforms, to better sustain SRL across forethought, performance, and reflection while lowering EF load.