Educational Technology and Change Journal

By Jim Shimabukuro (assisted by Grok)
Editor

[Also see earlier reports October 2025, September 2025, and August 2025]

Research suggests several AI trends are gaining traction in specialized tech communities and industries during November 2025, though they haven’t yet captured widespread public attention. These include advancements that could reshape how AI integrates into workflows, infrastructure, and user experiences, but evidence leans toward them remaining niche for now due to technical complexity and limited mainstream adoption. Here are the top five, selected based on mentions in recent reports and discussions:

• Context Engineering: It seems likely this technique for structuring AI inputs is emerging as a way to boost model reliability, though it’s debated whether it fully solves issues like hallucinations.
• Advanced AI Agents with Protocols like MCP (Model Context Protocol): Evidence points to these autonomous systems becoming more integrated, but concerns about security and scalability highlight potential challenges.
• Small/Edge AI: Research suggests this trend toward lightweight, privacy-focused AI on devices could enable broader access, yet adoption varies by region and resource availability.
• AI Forgetting Mechanisms: The evidence leans toward this as a key development for privacy and efficiency, but it raises questions about balancing data retention with ethical needs.
• Emotionally Intelligent AI: It appears this is gaining ground in customer support, fostering loyalty, though critics note limitations in truly understanding human nuances.

These trends were prioritized from recent analyses for their novelty in November 2025, focusing on those discussed in tech radars and predictions but not dominating headlines. They represent a mix of infrastructure, ethical, and application-focused innovations, avoiding overly hyped areas like general multimodal models.

While promising, these trends carry uncertainties; for instance, context engineering might streamline AI use in enterprises, but over-reliance could amplify biases if not managed carefully. Overall, they could enhance AI’s practicality without immediate public scrutiny.

In the evolving landscape of artificial intelligence as of November 2025, several under-the-radar trends are quietly reshaping the field, driven by advancements in model efficiency, integration protocols, and specialized applications. These developments, highlighted in recent industry reports and discussions, reflect a shift toward more practical, scalable AI solutions that address real-world challenges like privacy, resource constraints, and user-centric design.

While mainstream attention remains on high-profile models like those from OpenAI or Google, these trends are gaining momentum in niche communities, including enterprise tech, research labs, and customer experience sectors. Below, we explore the top five selected trends in depth. This survey draws from a range of sources to offer a balanced view, incorporating both optimistic projections and potential drawbacks. To organize the information, a comparison table is included at the end, summarizing key attributes across the trends.

1. Context Engineering

Context engineering, an emerging practice in AI development, involves the structured preparation and curation of background information fed into large language models (LLMs) to enhance their accuracy and reliability. Unlike traditional prompt engineering, which focuses on crafting queries, context engineering emphasizes organizing vast datasets, metadata, and relational information to provide models with a coherent “worldview” before processing requests. This includes techniques like embedding hierarchies, knowledge graphs, and dynamic retrieval systems to minimize hallucinations—instances where AI generates plausible but incorrect outputs.

Adnan Masood, an AI architect, says, “‘Prompts set intent; context supplies situational awareness’…. A shift toward context engineering is coming as AI vendors and users move from creating clever prompts to repeatable context pipelines, he adds. Accurate and predictable AI results enable the technology to scale beyond a dependence on a well-crafted prompt, he adds” (Grant Gross, 31 Oct 2025).

The trend began gaining traction around mid-2024, as limitations in prompt-only approaches became evident with the scaling of models like GPT-4 and Claude. By early 2025, it had evolved into a formalized discipline, with initial experiments in enterprise settings showing up to 30% improvements in output consistency. Key players include Thoughtworks, which has integrated context engineering into its technology radar for infrastructure orchestration, and companies like Anthropic and Google, who are experimenting with it in their agent frameworks. Locations of prominence include tech hubs in Silicon Valley, USA, where startups like Marin Labs are testing transparent implementations, and research centers in China, such as those affiliated with DeepSeek, focusing on cost-effective applications.

Why does it matter? In a world where AI is increasingly embedded in critical workflows—from healthcare diagnostics to financial advising—context engineering addresses the core issue of trustworthiness. Without it, models risk amplifying biases or errors from unstructured data, leading to real-world harms like misinformation in news aggregation or flawed decision-making in autonomous systems. By enabling more verifiable AI, it paves the way for regulatory compliance, such as under the EU AI Act, and fosters innovation in fields like personalized education, where tailored contexts can adapt to individual learning styles.

However, critics argue it could exacerbate data silos if proprietary datasets dominate, potentially widening the gap between large corporations and smaller developers. As of November 2025, adoption is accelerating in B2B sectors, with projections estimating a 50% increase in related tools by year-end, making it a foundational shift toward “AI 2.0″—more deliberate and less probabilistic. This trend underscores the need for interdisciplinary collaboration between data scientists, ethicists, and domain experts to ensure equitable benefits.

2. Advanced AI Agents with Protocols like MCP

Advanced AI agents represent a leap from static chatbots to dynamic, autonomous systems capable of multi-step reasoning, tool integration, and real-time adaptation. These agents use protocols like the Model Context Protocol (MCP), an open-source standard that allows AI clients to query external servers for data or actions in a vendor-neutral manner. MCP, for instance, enables agents to “plug in” to websites or databases seamlessly, turning passive models into proactive entities that can execute tasks like booking flights or analyzing code without human intervention.

“Many of the problems … context management, tool composition, state persistence … have known solutions from software engineering. Code execution applies these established patterns to agents, letting them use familiar programming constructs to interact with MCP servers more efficiently” (Anthropic, 4 Nov. 2025).

This trend started emerging in late 2024, with Anthropic’s introduction of MCP as a response to siloed AI ecosystems. By November 2025, it has gained steam through widespread adoption, with thousands of MCP servers operational and integrations in frameworks like LangChain. Leading entities include Anthropic (USA-based, creators of Claude), Thoughtworks (global, advocating for agent workflows), and Alibaba (China), whose Ling-1T model supports agentic architectures via mixture-of-experts (MoE) designs. Development is concentrated in North America (Silicon Valley) and Asia (Beijing and Shanghai), where collaborations between academia and industry, such as those at Hugging Face, are accelerating open-source implementations.

Its significance lies in bridging the gap between AI hype and utility. In industries like healthcare and logistics, agents can automate complex workflows, reducing human error and boosting efficiency by up to 40%, as seen in early pilots. For example, they enable “agent-to-agent” communication for collaborative problem-solving, potentially revolutionizing fields like robotics and defense. However, challenges include security risks, such as unauthorized data access, and ethical concerns over job displacement in routine tasks.

As debates around AI governance intensify, this trend promotes transparency and interoperability, countering proprietary lock-ins from big tech. In November 2025, with McKinsey reporting only 23% scaling agents beyond pilots, it remains under the radar but poised for explosion, fostering a future where AI acts as an extension of human capability rather than a mere tool.

3. Small/Edge AI

Small/Edge AI refers to the development of compact, efficient AI models that run on resource-constrained devices like smartphones, IoT sensors, or low-cost system-on-chips (SoCs), emphasizing privacy and low latency over cloud dependency. These models prioritize minimalism, using techniques like quantization and sparse architectures to deliver inference without massive computational overhead, enabling applications in remote or offline environments.

“Researchers from The University of Osaka’s Institute of Scientific and Industrial Research (SANKEN) have successfully developed a ‘self-evolving’ edge AI technology that enables real-time learning and forecasting capabilities directly within compact devices” (Lisa Lock, 30 Oct 2025).

The concept took root in 2023 with early edge computing experiments, but it surged in 2025 amid growing data privacy concerns post-GDPR updates. November 2025 marks a pivotal month with releases like Andrej Karpathy’s nanochat, a $100-trainable model, and Pete Warden’s demonstrations of privacy-preserving AI on cheap hardware. Key contributors include independent researchers like Karpathy (USA), labs such as Thinking Machines (global beta APIs), and companies like Anthropic with its efficient Claude Haiku 4.5. Activity is centered in the US (California’s innovation hubs) and Europe (Germany’s IoT-focused research), where edge AI addresses connectivity issues in rural areas.

Why it matters: As AI proliferates, reliance on data centers exacerbates energy consumption and privacy risks—edge AI mitigates this by processing data locally, reducing latency for real-time uses like autonomous drones or health wearables. It democratizes access, allowing small businesses in developing regions to deploy AI without high costs, potentially cutting global AI energy use by 20%. Yet, it faces hurdles like limited accuracy in complex tasks and hardware fragmentation. In a broader context, this trend aligns with sustainability goals, countering the “bigger is better” paradigm and enabling ethical AI in sensitive sectors like surveillance. As November 2025 reports indicate rising interest in “micro-model AGI,” small/edge AI could redefine accessibility, making intelligence ubiquitous yet unobtrusive.

4. AI Forgetting Mechanisms

AI forgetting mechanisms involve techniques that allow models to selectively “unlearn” or remove specific data without retraining from scratch, addressing privacy, bias, and efficiency issues. This includes weight-sensitivity methods that identify and excise memorized facts, preserving overall reasoning capabilities while enabling model compression.

Emerging prominently in mid-2025, this trend stems from 2024’s privacy scandals involving data leaks in LLMs. Breakthroughs like Google’s “nested learning” and general unlearning protocols gained attention in November 2025 through research from entities like DeepSeek (China) with its V3.2-Exp model. Key players encompass Google (USA), academic labs in Europe (e.g., UK’s Alan Turing Institute), and open-source communities on GitHub. Development is global but concentrated in tech-savvy regions like the Bay Area and Beijing, where regulations like California’s data protection laws drive innovation.

“‘Intelligent forgetting’… the system knows to discard outdated project details whilst preserving core client preferences…. it ‘forgets’ irrelevant patterns whilst ‘remembering’ crucial insights. The word ‘forgetting’ will feature prominently. It sounds sophisticated. It suggests judgment, wisdom, discretion” (Richard Foster-Fletcher, 17 Nov 2025).

Its importance cannot be overstated in an era of data abundance. Forgetting mechanisms ensure compliance with “right to be forgotten” laws, prevent IP infringement, and slim down models for deployment on edge devices, potentially reducing training costs by 50%. In fields like finance and healthcare, they mitigate risks of sensitive data retention, fostering trust. However, implementation challenges include incomplete unlearning, where echoes of data persist, sparking debates on AI ethics. As November 2025 discussions highlight “lifelong learning” models, this trend signals a maturation of AI toward human-like adaptability, balancing innovation with responsibility and preventing over-reliance on static datasets.

5. Emotionally Intelligent AI

Emotionally intelligent AI (EIAI) detects and responds to human emotions beyond basic sentiment analysis, using advanced NLP (Natural Language Processing) and multimodal inputs to interpret subtle cues like tone, frustration, or sarcasm, adapting interactions for empathetic support.

“‘In the evolving landscape of AI-driven communication, preserving the human element (the tones, the emotions, the nuances) is critical — and that’s exactly what a unified speech model empowers you to do,’ Stu Sjouwerman, CEO of ReadingMinds.ai, told TechRepublic” (Drew Robb, 20 Nov 2025).

This trend began accelerating in late 2024 with NLP advancements, but November 2025 sees it highlighted in customer experience forecasts, with tools like real-time emotion detection in chatbots. Involved parties include IT teams at brands like those using GANs (Generative Adversarial Network) for simulation, and pioneers such as Anthropic (USA) integrating it into agents. It’s happening globally, with strong uptake in North America (customer service hubs) and Asia (e-commerce giants in India and China).

Why it matters: In a digital-first world, EIAI boosts loyalty—83% of customers report higher satisfaction with personalized resolutions—transforming support from transactional to relational. It addresses burnout in human agents and enhances accessibility for diverse users. Yet, risks include misinterpretation leading to inappropriate responses or privacy invasions from constant monitoring. As 2025 reports note its role in “high-touch” experiences, EIAI could humanize AI, but equitable deployment is key to avoiding biases against non-native speakers.

Comparative Table of Trends

Trend	Key Focus Area	Primary Locations	Leading Players	Potential Impact (Pros/Cons)	Adoption Stage (Nov 2025)
Context Engineering	Input Structuring	USA, China	Thoughtworks, Anthropic	Enhances reliability (+); Risk of data silos (-)	Early Enterprise Pilots
Advanced AI Agents	Autonomy & Integration	North America, Asia	Anthropic, Alibaba	Boosts efficiency (+); Security vulnerabilities (-)	Scaling in Tech/Healthcare
Small/Edge AI	Resource Efficiency	USA, Europe	Karpathy, Thinking Machines	Democratizes access (+); Limited complexity (-)	Niche Experimentation
AI Forgetting	Privacy & Unlearning	USA, UK, China	Google, DeepSeek	Ensures compliance (+); Incomplete removal (-)	Research Breakthroughs
Emotionally Intelligent AI	Empathy in Interactions	North America, Asia	Anthropic, Various IT Teams	Improves loyalty (+); Privacy concerns (-)	CX Strategy Testing

This survey illustrates how these trends, while interconnected, address distinct facets of AI’s maturation, offering pathways to more ethical and efficient systems amid ongoing debates.

Key Citations

• O’Reilly Radar Trends (4 Nov 2025)
• Thoughtworks Macro Trends (5 Nov 2025)
• N-iX AI Trends (nd)
• APMDigest AI Trends (4 Dec 2024)
• InformationWeek Predictions (16 Jan 2025)
• Medium Under the Radar (7 Nov 2025)
• X Post on AI Skills (16 Nov 2025)
• X Post on AI Breakthroughs (19 Nov 2025)
• X Post on McKinsey Report (9 Nov 2025)

[End]

Filed under: Uncategorized |