Joshua Lawrence
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BEAM: Bridging Education, AI, and Measurement

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A flagship research initiative connecting AI-enabled assessment, inclusive measurement, and literacy research-practice partnerships.

Overview

BEAM—Bridging Education, AI, and Measurement—is the umbrella initiative for my current work on how new forms of assessment and AI-supported analysis can improve literacy research and practice. The project brings together measurement, vocabulary research, oral language assessment, implementation data, and school partnerships.

The central idea is that better tools should not simply produce more data. They should help educators, researchers, and families understand what students know, where they need support, and how instruction can be adapted in practical ways.

Official BEAM research group page at the University of Oslo

Why this matters

Schools are surrounded by data, but much of it is too broad, too delayed, or too disconnected from instruction. BEAM focuses on building assessment and data systems that are more useful: systems that identify meaningful learner profiles, support multilingual and struggling readers, and connect results to concrete instructional decisions.

Current work

  • Developing AI-supported approaches to assessment design and interpretation.
  • Connecting vocabulary, oral language, and reading data across age groups.
  • Building research infrastructure for rapid assessment and school-facing feedback.
  • Creating bridges among university research, school partnerships, and scalable tools.

Guiding questions

  • How can AI help create better literacy assessments without weakening validity or equity?
  • How can assessment results be translated into instructional supports that teachers can actually use?
  • What forms of measurement are most useful for multilingual learners and older struggling readers?

Members

The official UiO BEAM page is the source of truth for current membership. This snapshot reflects the public UiO member list.