RESEARCH
I study what changes when technology interacts with the conditions for learning.
My research examines how people think, communicate, and learn in AI-mediated, immersive, and data-rich environments. Across projects, I connect cognitive and interaction processes to questions of design, assessment, and educational access.
01
Human–AI Interaction
Dialogue, feedback, intelligent agents, generative AI, and learning.
02
Immersion & Cognition
Presence, attention, imagery, cognitive load, memory, and immersive media.
03
Learning Analytics & Assessment
Interaction traces, discourse, performance, validity, and evidence of learning.
AI-SUPPORTED TEACHER SIMULATION
01 · HUMAN–AI interaction
How do people communicate, reason, and learn with intelligent agents?
I study human interaction with generative AI and intelligent agents in learning contexts. One stream of this work uses AI-powered student agents in virtual teacher-training simulations to examine dialogue, instructional decision making, and academically productive discourse. A second stream investigates LLM-supported writing feedback and automated assessment, with particular attention to validity, reliability, and how learners experience AI feedback.
CURRENT QUESTIONS
- How do agent behaviors and affective profiles shape learner dialogue?
- When does AI feedback support rather than distort learning?
- What evidence is sufficient to trust AI-based assessment?
Representative paper
Preservice teachers’ dialogic interactions with AI-powered student agents: Patterns and perceptions ↗
02 · IMMERSION & COGNITION
What makes an experience immersive—and when does immersion actually help learning?
My immersive-learning research treats immersion as more than a property of a device. I examine how presence, attention, mental imagery, cognitive load, and memory interact across virtual reality and narrative media. This work asks when immersive experiences deepen learning, when they merely intensify experience, and which instructional supports make immersion educationally useful.
CURRENT QUESTIONS
- How do immersion, imagery, load, and memory operate together?
- Can immersive effects transfer across media and modalities?
- How might neurodiverse learners experience immersive stimuli differently?
Representative paper
The virtual reality in our head: How immersion and mental imagery are connected to knowledge retention ↗
IMMERSIVE LANGUAGE-LEARNING ENVIRONMENT
INTERACTION + PERFORMANCE DATA
03 · LEARNING ANALYTICS & ASSESSMENT
How can traces of interaction become credible evidence about learning?
Across AI-supported simulations and virtual learning environments, I use language, behavioral traces, and performance data to investigate learning processes that are difficult to observe with outcome measures alone. This includes pattern and discourse analysis, evidence-centered modeling, longitudinal analysis, and validity and reliability studies of emerging forms of assessment.
CURRENT QUESTIONS
- Which interaction patterns meaningfully distinguish productive learning?
- How should AI-generated scores and feedback be validated?
- How can behavioral data support stronger claims about learning processes?
Representative paper
Large language models and automated essay scoring of English language learner writing: Insights into validity and reliability ↗
CROSS-CUTTING WORK
Access, neurodiversity & educational ecosystems.
A recurring question across my work is who emerging learning environments are designed to support. My research has examined computational-thinking behaviors of neurodiverse adolescents in virtual worlds, the fidelity of virtual maker-education interventions, and broader educational and care ecosystems for neurodiverse populations.
HOW I STUDY LEARNING
Methods follow the question.
I combine design, behavioral data, qualitative evidence, and quantitative modeling to study not only whether an intervention works, but what happens during the learning process.
01
Design-based research
Building and iterating learning environments while studying use in context.
02
Experimental & longitudinal analysis
Testing change across conditions, time, and repeated learning experiences.
03
Discourse & qualitative analysis
Examining dialogue, think-aloud data, perceptions, and experience.
04
Pattern & computational analytics
Using sequences and interaction traces to model learning processes.
05
Measurement & validation
Evaluating reliability, validity, and evidence-centered assessment claims.
WHERE THE WORK IS GOING
Questions I want to answer next.
HUMAN–AI
How should intelligent agents behave when their behavior itself becomes part of the learning environment?
IMMERSION
Which combinations of media, guidance, and learner characteristics turn immersion into durable learning?
EVIDENCE
Alex Barrett 