Self-Regulated Learning

Self-regulated learning (SRL) describes learners as active participants who can shape and develop their cognitive and behavioral actions in a successful way. AI tools can either scaffold SRL development or inadvertently short-circuit it by removing the regulatory demands that build expertise.^Scheu Mobile Chatbot Journaling Motivation 2026^Stanford Evidence Base AI K12 2026

SRL is the process whereby learners actively manage their own learning through three interrelated phases:

1. Forethought: Goal setting, strategic planning, self-efficacy beliefs

2. Performance: Strategy deployment, self-observation, attention focusing

3. Self-reflection: Self-evaluation, causal attribution, adaptation

Proficient self-regulated learners employ cognitive strategies to improve success and utilize Metacognition to refine their learning processes continuously.^Scheu Mobile Chatbot Journaling Motivation 2026

Digital Support for SRL

Learning Journals

Learning journals are a promising SRL intervention: by reflecting on their learning processes, students increase awareness of cognition and strengthen regulatory capacity. Key design considerations:

Structure matters: Open-ended journals often produce shallow entries; guided prompts and example models improve depth

Motivation decay: Mobile journaling apps commonly see rapid engagement decline after a few days

Scaffolding trade-off: AI assistance that writes reflections for students undermines the SRL practice; assistance that structures prompts without authoring content preserves it

Scheu et al.'s 2×2 Experiment (2026)

In a randomized field experiment with 179 students over 22 days, two design principles were compared:

Principle	Mechanism	Effect on SRL	Effect on Motivation	Effect on Engagement
Example-based course	7-day curriculum teaching reflective journaling via modeled responses	Increased perceived competence and enjoyment	Positive	Constant positive
LLM journaling assistant	GPT-3.5 summarizes drafts, asks clarifying questions, suggests reformulations	No direct SRL skill effect measured	No effect	Increasing over time (feedback loop)

Key insight: The course improved SRL skills and intrinsic motivation through skill transfer, while the assistant improved engagement without affecting motivation.^Scheu Mobile Chatbot Journaling Motivation 2026

AI Tools and the SRL–Motivation Reciprocal Loop

A foundational principle of SRL theory is that self-regulation skills and motivation form a reciprocal relationship:

Better SRL → more successful learning → higher self-efficacy → stronger motivation

Higher motivation → more effortful engagement → better SRL practice

AI tools can enter this loop at different points:

SRL-first design (e.g., structured courses, graduated hints, reflection prompts): Strengthens the loop by building genuine skill

Engagement-first design (e.g., autocomplete, content generation): May boost behavioral engagement without entering the motivation loop, risking tool dependence

Relationship to Tutoring-Specific Design

Tutoring-specific AI aligns with SRL-first design: it provides graduated scaffolds that preserve learner agency and require strategic self-regulation. General-purpose AI often removes the regulatory demands entirely.^Stanford Evidence Base AI K12 2026

For example:

Bastani et al.'s tutoring-specific chatbot preserved step-by-step reasoning (SRL demand)

The general-purpose GPT variant simply provided answers (SRL bypass)