🏷️ faculty-development-genai
20 pages tagged with faculty-development-genai(20 articles, 0 concepts)
📄 Enhancing learner-centered feedback with AI: teachers'' practices and perceptions
An empirical study of **21 higher-education teachers** using **PolyFeed**, an AI-powered feedback tool combining (1) a **BERT-based ML model** (from Aldino et al. 2024) that detects which learner-cent…
📄 Programming Language Policy as an AI Literacy Equity Problem: A 15-Nation Comparative Analysis
Across 15 nations, the paper examines how secondary computer-science education embeds AI literacy into general-track subjects (Digital Literacy, ICT, TIC, SNT) rather than specialized tracks, creating…
📄 A Durability and Cross-Language Transfer Benchmark for a Validated Teaching-Feedback Classification Protocol
> Extends a prior validated protocol for classifying open-ended teaching-evaluation feedback by thematic category and sentiment, introducing a durability and cross-language transfer benchmark. Institu…
📄 Faculty Orientations Shape Adoption of AI in Research and Teaching
📄 arXiv · [PDF](https://arxiv.org/pdf/2605.18140) A mixed-methods survey of 90 STEM faculty in the RCSA Cottrell community identified a coherent latent construct — **AI pedagogical orientation** — th…
📄 Modeling AI-TPACK in Practice: Insights from Teachers'' Multi-Agent Workflow Design
This study investigates how teachers design multi-agent instructional workflows and identifies three distinct **teacher archetypes** that emerge from behavioral log analysis of 61 in-service teachers:…
📄 Characterizing Students' LLM Usage Behaviors and Their Association with Learning in Critical Thinking Tasks
> Characterizing Students' LLM Usage Behaviors and Their Association with Learning in Critical Thinking Tasks **Park, Orozco Vasquez, & Conati (2026)** — University of British Columbia. Accepted at ED…
📄 AI-Generated Slides: Are They Good? Can Students Tell?
This study evaluated five generative AI tools for creating instructional slides from instructor-authored course notes: NotebookLM, Claude, M365 Copilot, Cursor, and Claude Code. Educators assessed sli…
📄 A Framework for Institutional Change in the Age of AI
> Perl-Nussbaum & Finkelstein (2026) adapt institutional-change models to generative AI as an **arrival technology** — one that entered classrooms before pedagogical evidence existed — yielding a six-…
📄 AI Adoption Among Teachers: Insights on Concerns, Support, Confidence, and Attitudes
A study of 260 Filipino teachers examined how institutional support, teacher confidence, and teacher concerns influence AI adoption attitudes: This paper provides empirical clarity for [[teacher-role]…
📄 TeachingCoach: A Fine-Tuned Scaffolding Chatbot for Instructional Guidance to Instructors
> **Authors:** Isabel Molnar, Peiyu Li, Si Chen, Sugana Chawla, James Lang, Ronald Metoyer, Ting Hua, Nitesh V. Chawla **Year:** 2026 **Venue:** arXiv (cs.AI) > **Year:** 2026 > **Venue:** arXiv (cs.A…
📄 Scaffolding Critical Thinking with Generative AI
> Vendrell & Johnston (2026) propose a design-oriented framework for LLM use in higher education that strengthens rather than displaces [[critical-thinking]], countering [[cognitive-offloading]] and m…
📄 Guidelines for Designing AI Technologies to Support Adult Learning
> A set of 19 empirically-grounded design guidelines for AI-supported learning technologies tailored to adult learners, synthesized by Reddig et al. (2026) from longitudinal deployment data at a US na…
📄 What AI in Education Needs Next: Lessons from Youth Leaders Across Five Countries
> A global perspective on AI in education readiness, framed around the insight that the real bottleneck is human and institutional capacity, not technical access. Based on a WEF (2026) synthesis of yo…
📄 The Impact of AI on Work in Higher Education
> A large-scale survey (n=1,960) by EDUCAUSE (2026) examining how AI is reshaping work in higher education institutions — attitudes, adoption patterns, institutional strategies, risks, and opportuniti…
📄 How State Policy Can Help Teachers Use AI Well
> A NASBE/CRPE policy analysis (May 2026) examining how US states can shape conditions for effective teacher AI adoption — setting guardrails, providing resources, and building capacity without microm…
📄 Multi-Agent Systems for Instructional Design
> Embedding the Knowledge–Learning–Instruction (KLI) framework into multi-agent systems to act as sophisticated instructional designers for K-12 educators.…
📄 The University AI Didn''t Replace: Rethinking Universities in the AI Era
> **Synthesis:** Rather than replacing universities, generative AI **redefines their essential functions** — this paper proposes a four-level framework of institutional AI adoption and argues that the…
📄 AI Tutor Safety and Pedagogical Harms
> Conventional LLM safety benchmarks focus on toxic outputs, jailbreaks, and bias. In education, the primary risks are quieter: > "Solving problems correctly and avoiding toxic language does not make …
📄 Educational LLM Alignment
> Hardy & Kim (2026) identify a **cascading proxy** problem in AI-for-education evaluation: > The gap between what LLMs are *capable* of and what actually *benefits learners* — benchmark performance, …
📄 Principled AI in Education
> The framework rests on three interconnected anchors that must be addressed *before* selecting tools: > Rejecting the binary promise-vs-peril discourse and the rush to immediate implementation, Finke…