Engagement intensity as a pair of complementary intake features (reported use and self-rated familiarity) for learner modeling

Design · All designs
Evidence · 2 claims (2 for) · 1 study (1 associational), q2 · 1 of 1 report an effect size · 2 claims rest on one study

Description

The article introduces engagement intensity as a working label for two complementary intake indicators of technology engagement drawn from technology acceptance and AI literacy research: "reported use and self-rated familiarity." It functions as a lightweight learner-modeling signal for intake-level grouping, explicitly scoped to pre-instruction segmentation rather than adaptive sequencing, personalized feedback, or a full intelligent tutoring system. In this framing, behavioral usage frequency is the primary axis and familiarity a secondary, self-perception-based check on perceived readiness.

Design Implications

Context

Requirements

• Both features must be collected as short pre-instruction survey items before instruction begins

Constraints

• The article defines adaptive as intake-level learner grouping only, not differentiated materials, adaptive sequencing, personalized feedback, or a full intelligent tutoring system

Target Learners

• graduate and postdoctoral bioscience trainees in research ethics instruction

Learning Goals

• intake-level learner profiling for adaptive AI ethics instruction

Claims

• Usage Frequency Associates All Five Baseline Ai Perceptions [+M]
• Llm Familiarity Weaker Secondary Intake Signal [+M]

Related Designs

Examples

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Key Sources

• Yongkyung Oh, Lynn Talton, Alex Bui. (2026). Engagement Intensity as a Learner-Modeling Signal for Adaptive AI Ethics Instruction. KDD 2026 AI for Education Day, at the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '26). https://kdd2026.kdd.org

Page metadata

Field	Value
type	design
id	engagement-intensity-intake-construct
title	Engagement intensity as a pair of complementary intake features (reported use and self-rated familiarity) for learner modeling
description	The article introduces engagement intensity as a working label for two complementary intake indicators of technology engagement drawn from technology acceptance and AI literacy research: "reported use and self-rated f...
status	review
generated	by process:settle-candidates on 2026-10-10
trust	unverified — no one has confirmed this page's content
sources	1 — yongkyung-oh-2026

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