Teaching Life as a System: The Structured Life Systems Method for Neurodivergent Learners

This methodological paper provides a shortened overview of the Structured Life Systems Method (SLSM), an educational framework for structured life-skills instruction and functional autonomy among neurodivergent learners. The Structured Life Systems Method (SLSM) is an educational framework developed to support structured functional autonomy in neurodivergent adolescents and adults who require low-to-moderate levels of cognitive and functional support. The method is based on the premise that everyday life systems can be taught through explicit structure, staged progression, guided practice, repetition with variation, and scenario-based learning. Drawing on principles associated with executive functioning, working memory, adult learning, and structured pedagogy, SLSM presents practical life domains, such as financial decision-making, transportation, safety, planning, and independent living, as teachable systems. The method is not designed as preparation for higher education or university-level academic study; rather, it supports functional learning, practical decision-making, and structured participation in everyday contexts. It does not function as a clinical intervention; rather, it offers an educational architecture for helping learners organize information, compare options, rehearse decisions, and transfer structured thinking into real-life situations.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-05-10
DOI
https://doi.org/10.5281/zenodo.20113626
Primary Topic
Neuroscience, Education and Cognitive Function
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Teaching Life as a System: The Structured Life Systems Method for Neurodivergent Learners

Khrystyna Koval
Zenodo (CERN European Organization for Nuclear Research)
Neuroscience, Education and Cognitive Function
article

Teaching Life as a System: The Structured Life Systems Method for Neurodivergent Learners

Khrystyna Koval
article en

Abstract

This methodological paper provides a shortened overview of the Structured Life Systems Method (SLSM), an educational framework for structured life-skills instruction and functional autonomy among neurodivergent learners. The Structured Life Systems Method (SLSM) is an educational framework developed to support structured functional autonomy in neurodivergent adolescents and adults who require low-to-moderate levels of cognitive and functional support. The method is based on the premise that everyday life systems can be taught through explicit structure, staged progression, guided practice, repetition with variation, and scenario-based learning. Drawing on principles associated with executive functioning, working memory, adult learning, and structured pedagogy, SLSM presents practical life domains, such as financial decision-making, transportation, safety, planning, and independent living, as teachable systems. The method is not designed as preparation for higher education or university-level academic study; rather, it supports functional learning, practical decision-making, and structured participation in everyday contexts. It does not function as a clinical intervention; rather, it offers an educational architecture for helping learners organize information, compare options, rehearse decisions, and transfer structured thinking into real-life situations.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 5%
Neuroscience, Education and Cognitive Function
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.