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Archived digest · Week of Dec 15 - Dec 21, 2025

Applied AI news,
scored for your field

Each week the Institute for Applied AI Innovation reviews AI publications and scores them for Research Relevance, Educational Value, Innovation/Novelty, Practical Impact, Interdisciplinary Potential and Ethical/Policy Implications. Then it writes summaries for each discipline at UTEP.

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The Week at a Glance

Education & Leadership · Dec 15 - Dec 21, 2025

Education & Leadership. Teacher education, educational leadership, engineering education. Prefers pedagogy, learning science, edtech, and equity in STEM.
Departments: Educational Leadership, Engineering Education & Leadership, Teacher Education
Education & Leadership

Weekly Summary of Top AI Articles

This week's articles focus on the evolving landscape of AI in education and leadership, emphasizing the importance of psychological safety, realistic expectations, and user understanding. A survey highlights the need for psychological safety in AI initiatives, while a new series aims to recalibrate expectations surrounding AI capabilities. Additionally, prompt engineering and RAG systems are discussed as vital components for enhancing data quality and user trust in AI applications.

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Key Findings
  • Psychological safety is crucial for successful enterprise-level AI initiatives.
  • There is a significant gap between the perceived potential of AI and its actual capabilities.
  • Understanding user perspectives is essential for designing effective AI tools.
Implications
  • Organizations must foster a culture of psychological safety to maximize AI adoption.
  • Resetting expectations can lead to more realistic and achievable AI goals.
  • User trust and knowledge levels should inform the design and implementation of AI systems.

Key Metrics

Numbers reported in that week's stories
500Business leaders surveyed on psychological safety in AI
Projected market growth for agentic systems from $5 billion
Four practical applications of Google AI Studio for data scientists
Weekly summary for Education & Leadership

Education & Leadership

Top articles by AAII Impact Score (out of 30).

Browse the archive ›
No. 1 · Psychology

Creating psychological safety in the AI era

Business PsychologyPolitical Science & Public AdministrationEducational Leadership
· 12/16/2025
23/30 AAII Impact Score

AI Summary: A survey conducted by MIT Technology Review Insights involving 500 business leaders highlights the critical role of psychological safety in the success of enterprise-level AI initiatives. The findings indicate that while a majority of executives recognize the importance of a culture that fosters psychological safety—83% believe it enhances AI project success—there remains a significant disconnect between this belief and the actual workplace environment, with 22% of leaders hesitant to lead AI projects due to fear of blame. The report emphasizes that psychological barriers are more significant obstacles to AI adoption than technological challenges, with less than half of leaders rating their organization's psychological safety as "very high." To effectively build psychological safety, the report suggests that organizations must integrate it into their collaboration processes rather than relying solely on HR initiatives.

Topics: Enterprise AIPsychological SafetyAI Adoption BarriersOrganizational Culture
AI Rubric Scores
Research Relevance
3
Educational Value
4
Innovation/Novelty
3
Practical Impact
5
Interdisciplinary Potential
4
Ethical/Policy Implications
4
Read the full article ›
No. 2 · Computer Science 23/30

Why it’s time to reset our expectations for AI

· 12/16/2025
Policy & Ethics Computer SciencePolitical Science & Public AdministrationIndustrial, Manufacturing & Systems EngineeringEngineering Education & Leadership

AI Summary: The article introduces a series titled "Hype Correction," which aims to reassess the current state and expectations of artificial intelligence (AI) technologies. It highlights concerns about the disconnect between AI's perceived potential and its actual capabilities, questioning the long-term value of investments in AI amidst issues like environmental impact. Contributions from various authors examine topics such as the role of AI in job displacement, the effectiveness of AI in coding, and the challenges in AI-driven materials discovery. Overall, the series calls for a critical evaluation of AI's promises versus its realities.

Topics: AI EthicsJob DisplacementAI in CodingMaterials Discovery Challenges
AI Rubric Scores +
Research Relevance
3
Educational Value
4
Innovation/Novelty
3
Practical Impact
4
Interdisciplinary Potential
4
Ethical/Policy Implications
5
Read Full Article ›
No. 3 · Computer Science 23/30

Prompt Engineering for Data Quality and Validation Checks

· 12/18/2025
Applications Computer ScienceEngineering Education & Leadership

AI Summary: Prompt engineering is emerging as a crucial technique for enhancing data validation processes by enabling large language models (LLMs) to function as intelligent auditors. Unlike traditional rule-based validation, which relies on static conditions, prompt engineering allows for context-aware assessments that can identify inconsistencies and errors in unstructured or semi-structured data. This approach emphasizes the importance of crafting prompts that mimic human reasoning, incorporating clarity, context, and domain knowledge to improve the model's ability to evaluate data coherence. By supplementing existing validation checks, well-structured prompts can enhance the detection of subtle data issues, ultimately leading to more effective quality assurance practices.

Topics: Natural Language ProcessingPrompt EngineeringData Quality ValidationContext-Aware Assessment
AI Rubric Scores +
Research Relevance
4
Educational Value
4
Innovation/Novelty
5
Practical Impact
4
Interdisciplinary Potential
3
Ethical/Policy Implications
3
Read Full Article ›
No. 4 · Computer Science 23/30

Understanding the Generative AI User

· 12/20/2025
Applications Computer ScienceEducational Leadership

AI Summary: The article discusses the importance of understanding user perspectives when designing tools based on large language models (LLMs). It highlights that users may have varying levels of knowledge and trust regarding AI, which can significantly influence their interaction with LLM-based products. The authors reference the Four-Persona Framework developed by researchers at Indiana University, which categorizes users into four archetypes: Unconscious, Avoidant, Enthusiast, and Informed AI User. The article emphasizes that effective product design must consider these user profiles to avoid assumptions that could lead to product failure.

Topics: Generative AIUser Persona FrameworkUser Trust in AILLM Interaction Design
AI Rubric Scores +
Research Relevance
4
Educational Value
5
Innovation/Novelty
3
Practical Impact
4
Interdisciplinary Potential
4
Ethical/Policy Implications
3
Read Full Article ›
No. 5 · Computer Science 23/30

Six Lessons Learned Building RAG Systems in Production

· 12/19/2025
Business Computer ScienceEngineering Education & Leadership

AI Summary: The article discusses the importance of implementing effective Retrieval-Augmented Generation (RAG) systems in AI projects, emphasizing that a poorly designed RAG system can lead to user distrust and abandonment. It outlines six key lessons for successful RAG deployment, starting with the necessity of addressing a real business problem and ensuring measurable value before development. The author highlights that data preparation is often underestimated and critical for system performance, asserting that high-quality data is essential to avoid the pitfalls of misinformation. Ultimately, the article argues that RAG should be treated as a foundational infrastructure rather than a fleeting trend.

Topics: Generative AIRetrieval-Augmented GenerationData PreparationUser Trust in AI
AI Rubric Scores +
Research Relevance
4
Educational Value
5
Innovation/Novelty
3
Practical Impact
4
Interdisciplinary Potential
3
Ethical/Policy Implications
4
Read Full Article ›
No. 6 · Computer Science 21/30

AgentOps Learning Path 2026

· 12/19/2025
Education Computer ScienceEngineering Education & Leadership

AI Summary: The article outlines a structured six-month learning roadmap for mastering AgentOps, a discipline focused on developing intelligent AI agents capable of complex tasks. It emphasizes the growing market for agentic systems, projected to expand from $5 billion in 2024 to $50 billion by 2030, highlighting the need for reliable and cost-efficient production-ready systems. The initial phase of the roadmap includes assessing foundational skills in Python programming, API development, machine learning, and version control, which are essential for building and deploying AI agents. Subsequent months will cover agent architecture, frameworks, and practical implementation, aiming to equip learners with the necessary skills to create effective AI agents.

Topics: Autonomous SystemsAgent ArchitectureIntelligent AI AgentsProduction-Ready Systems
AI Rubric Scores +
Research Relevance
3
Educational Value
5
Innovation/Novelty
4
Practical Impact
4
Interdisciplinary Potential
3
Ethical/Policy Implications
2
Read Full Article ›
No. 7 · Computer Science 21/30

2025 Must-Reads: Agents, Python, LLMs, and More

· 12/19/2025
Applications Computer ScienceEngineering Education & Leadership

AI Summary: In 2025, the concept of agentic AI gained significant traction, becoming a focal point for practitioners in the field. Key contributions included tutorials and guides on designing AI agents, exploring the trade-offs between autonomous agents and orchestrated workflows, and building functional agents using Python. Notable articles covered topics such as the distinctions between single and multi-agent systems, as well as essential skills for career advancement in machine learning. The year also saw a continued emphasis on prompt engineering and the evolution of retrieval-augmented generation within the context of agentic AI.

Topics: Agentic AIMulti-Agent SystemsPrompt EngineeringRetrieval-Augmented Generation
AI Rubric Scores +
Research Relevance
4
Educational Value
5
Innovation/Novelty
4
Practical Impact
3
Interdisciplinary Potential
3
Ethical/Policy Implications
2
Read Full Article ›
No. 8 · Computer Science 21/30

4 Ways to Supercharge Your Data Science Workflow with Google AI Studio

· 12/18/2025
Applications Computer ScienceEngineering Education & Leadership

AI Summary: The article discusses the integration of Gemini 3 models into Google AI Studio, highlighting its utility for data scientists in enhancing productivity and learning. It presents four practical applications of the platform, including the creation of interactive tools for understanding complex concepts, such as Gaussian Processes, through a user-friendly interface that generates code based on plain language descriptions. Additionally, it emphasizes the ability to quickly build interactive prototypes for stakeholder engagement, allowing users to visualize and test models without extensive coding. The author shares personal insights and demonstrations, underscoring the platform's potential for streamlining data science workflows.

Topics: Generative AIInteractive PrototypingCode Generation from TextUser-Friendly Data Science Tools
AI Rubric Scores +
Research Relevance
3
Educational Value
4
Innovation/Novelty
4
Practical Impact
5
Interdisciplinary Potential
3
Ethical/Policy Implications
2
Read Full Article ›
No. 9 · Computer Science 19/30

Generative AI hype distracts us from AI’s more important breakthroughs

· 12/15/2025
Applications Computer SciencePublic Health SciencesNursingEngineering Education & Leadership

AI Summary: The article contrasts generative AI with predictive AI, emphasizing the latter's practical applications and advancements over the past two decades. While generative AI garners attention for its creative capabilities, predictive AI has significantly improved in areas such as weather forecasting, medical diagnostics, and object recognition. The author highlights that predictive AI has achieved remarkable accuracy in tasks like identifying species and detecting health issues, which has led to enhanced safety and efficiency in various sectors, including transportation and emergency response. This progress underscores the critical role of predictive AI in everyday life, often unnoticed but essential for improving quality of life.

Topics: Predictive AIMedical DiagnosticsWeather ForecastingObject Recognition
AI Rubric Scores +
Research Relevance
3
Educational Value
4
Innovation/Novelty
2
Practical Impact
5
Interdisciplinary Potential
3
Ethical/Policy Implications
2
Read Full Article ›
No. 10 · Computer Science 19/30

The Machine Learning “Advent Calendar” Day 17: Neural Network Regressor in Excel

· 12/17/2025
Applications Computer ScienceEngineering Education & Leadership

AI Summary: The article presents a step-by-step guide to building a neural network regressor from scratch using Excel, emphasizing transparency in the computations involved. It outlines the process of forward propagation, where the model's structure is defined with one input layer, one hidden layer containing two neurons, and an output layer, allowing for the approximation of non-linear relationships. The article also details the backpropagation process using gradient descent to minimize the mean squared error (MSE) and highlights the importance of understanding the underlying mathematical functions rather than viewing the neural network as a black box. Ultimately, the implementation aims to demystify neural networks by making all computations explicit and accessible.

Topics: Machine Learning EducationNeural Network RegressorForward PropagationBackpropagation Techniques
AI Rubric Scores +
Research Relevance
3
Educational Value
5
Innovation/Novelty
2
Practical Impact
3
Interdisciplinary Potential
4
Ethical/Policy Implications
2
Read Full Article ›
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