Respondents anticipated using LLMs most often (23%), followed by NLP (17%), predictive AI (14%), chatbots (13%), and ASR (10%)¶
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Evidence · 1 study · associationalr1·q2
Currency · newest study 2024 · checked against its sources 2026-10-09 (automated, 1 of 1 entry) · not reviewed by a person
Subclaims¶
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q2 i? Among selected technologies, LLMs were most anticipated (23%), followed by NLP (17%), predictive AI (14%), chatbots (13%), and augmented speech recognition (10%). → tech-types
Evidence¶
tech-types¶
Noakes, S., Shell, A., Van Nostrand, P., Murillo, A., Ruiz, P., and Liberman, B. (2024, September). AI-Powered Innovations in Mathematics Teaching & Learning: Initial Findings. Digital Promise. https://doi.org/10.51388/20.500.12265/229
q2 · i? · associational · r1
Descriptive statistics on technology selections across projects (Figure 5, N=666 selected responses). The report adds that only five submissions anticipated robotics and over 40 respondents indicated other technologies not on the list.
"Nearly a quarter (23%) anticipate using Large Language Models (LLMs), followed by Natural Language Processing (NLP) (17%), predictive AI (14%), chatbots (13%), and Augmented Speech Recognition (ASR) (10%)."
Discussion¶
Related Claims¶
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Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
rfi-technology-types-llm-most-common |
title |
Respondents anticipated using LLMs most often (23%), followed by NLP (17%), predictive AI (14%), chatbots (13%), and ASR (10%) |
description |
Respondents anticipated using LLMs most often (23%), followed by NLP (17%), predictive AI (14%), chatbots (13%), and ASR (10%) |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2024 |
source_checked |
2026-10-09 |
generated |
by process:wiki-ingest on 2026-10-09 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — tech-types |
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