307 件 ・ 関連度順
概要と使いどころ
When a learner gets something wrong or feels stuck, require them to diagnose the problem before receiving help. Ensures help targets the actual cognitive breakdown, not just the surface error.
日本語の概要は準備中です。原文の説明を表示しています。
Wrap a learning session in a plan → monitor → reflect cycle. Use at the start of any substantial study session to set goals, mid-session to check strategy, and at session end to consolidate what changed. Builds self-regulated learning as a habit.
日本語の概要は準備中です。原文の説明を表示しています。
Design metacognitive checkpoints that prevent AI-assisted learning from bypassing genuine understanding. Use when students use AI tools and may overestimate their own comprehension.
日本語の概要は準備中です。原文の説明を表示しています。
Require the learner to explain a concept in their own words before the AI evaluates or extends it. Ensures the AI works from the learner's understanding rather than providing an explanation from scratch.
日本語の概要は準備中です。原文の説明を表示しています。
Analyse existing written feedback for quality, specificity, actionability, and impact on student learning. Use when reviewing teacher or peer feedback to improve feedback practices.
日本語の概要は準備中です。原文の説明を表示しています。
Capture confidence ratings before and after a learning attempt to identify overconfidence and underconfidence patterns. Use when a student wants to understand how well they actually know something versus how well they think they know it.
日本語の概要は準備中です。原文の説明を表示しています。
Design a Funhouse Mirror activity where students use their own domain expertise to detect AI distortions, omissions, and overconfidence. Use when students know a subject well enough to evaluate AI claims about it.
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Use when answering questions about IFRS standards, IAS standards, IASB, financial reporting, revenue recognition, lease accounting, impairment, financial instruments, expected credit loss, ECL, consolidation, business combinations, fair value measurement, first-time adoption, deferred tax, provisions, insurance contracts, journal entries, disclosure requirements, IFRS compliance checks, audit support, GAAP differences, goodwill, or transitioning from local GAAP to IFRS.
日本語の概要は準備中です。原文の説明を表示しています。
Expert guide for AI image generation (Flux, DALL-E, Stable Diffusion), video generation (Sora, Runway), voice synthesis (ElevenLabs TTS), and speech recognition (Whisper STT) integration / Panduan ahli integrasi AI generasi gambar, video, suara (TTS), dan pengenalan suara (STT).
日本語の概要は準備中です。原文の説明を表示しています。
Conjecture generation, sequence analysis, and experimental mathematics for mathematical discovery. Covers sequence analysis (OEIS lookups, recurrence relations, generating functions), pattern detection methods (finite differences, ratio analysis, modular patterns), conjecture generation workflow, classical integer sequences (Fibonacci, Catalan, partition numbers, Bernoulli numbers), combinatorial identities, the probabilistic method (Erdos, expected value arguments, Lovasz Local Lemma), AI-assisted discovery (FunSearch, Ramanujan Machine), and confidence calibration with cautionary tales (Mertens conjecture, Polya conjecture). Use when discovering patterns, analyzing sequences, generating conjectures, or working with combinatorial identities.
日本語の概要は準備中です。原文の説明を表示しています。
Identifying species in the field using dichotomous keys, gestalt recognition, diagnostic features, and habitat context. Covers the working identification protocol from first encounter through confidence-rated record, the vocabulary of field marks, the discipline of negative evidence, and the honest reporting of uncertain IDs. Use when the task is to name a living organism encountered in the field.
日本語の概要は準備中です。原文の説明を表示しています。
Distributional analysis of environmental benefits and burdens across communities. Covers the historical origins of environmental justice in the U.S. civil rights tradition, siting and exposure disparities, indigenous rights and land stewardship, climate justice at global and intergenerational scales, procedural vs. distributional vs. recognition justice, and the environmental justice screening tools used by regulators. Use when analyzing who bears environmental harm and who receives environmental benefit, or when designing interventions whose distributional consequences matter.
日本語の概要は準備中です。原文の説明を表示しています。
Being a constructive participant in networked communities. Covers digital footprint management, online etiquette and professional communication, collaborative tools, attribution and copyright, cyberbullying recognition and response, and the responsibilities that come with amplification. Use when helping learners think about how to behave online -- not what is technically allowed, but what is actually healthy for themselves and their communities.
日本語の概要は準備中です。原文の説明を表示しています。
Recognition and mitigation of systematic reasoning errors documented in the heuristics and biases tradition. Covers confirmation bias, availability, anchoring, representativeness, framing effects, hindsight bias, overconfidence, and motivated reasoning. Each bias is presented with its mechanism, diagnostic signal, and structured mitigation. Use when you suspect your own or another reasoner's conclusions may be shaped by systematic cognitive distortion rather than evidence.
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Entrepreneurship, innovation, and disruption for starting, scaling, and defending new ventures. Covers opportunity recognition, jobs-to-be-done, the disruptive innovation framework, sustaining vs disruptive innovation, platform businesses, network effects, minimum viable product, customer development, and the startup lifecycle from idea to exit. Use when evaluating a venture idea, positioning a new product, interpreting a competitive threat, or designing a platform.
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Computational thinking as a problem-solving discipline independent of programming languages. Covers the four pillars (decomposition, pattern recognition, abstraction, algorithm design), computational problem-solving methodology (understand, decompose, generalize, formalize, verify), abstraction levels (from hardware through user interface), modeling and simulation, automata and formal languages (DFA, NFA, regular expressions, context-free grammars, Turing machines), computational complexity classes (P, NP, NP-complete, undecidable), and constructionist pedagogy (learning by building, Logo, Scratch, physical computing). Use when approaching unfamiliar problems, teaching problem-solving strategies, analyzing problem complexity, or bridging between domain knowledge and computational solutions.
日本語の概要は準備中です。原文の説明を表示しています。
Practical naked-eye and binocular sky observing. Covers dark adaptation, limiting magnitude, constellation recognition, star hopping, the messier and Caldwell catalogs accessible without a telescope, the Moon, planets, meteor showers, aurorae, and the ethics and habits of observing under light-polluted and dark skies. Use when teaching someone to find their way around the sky, planning a naked-eye session, or choosing a first binocular tour.
日本語の概要は準備中です。原文の説明を表示しています。
Run a custom computer-vision prompt against an image with a local vision LLM. Wraps kuzco's tool_llm_image_custom() ellmer tool. Use when none of the specialized kuzco image skills (classification, OCR, recognition, sentiment, alt text) fit the task.
日本語の概要は準備中です。原文の説明を表示しています。
Binary visualization for human pattern recognition - Ghidra plugin by Chris Domas (xoreaxeaxeax)
日本語の概要は準備中です。原文の説明を表示しています。
Apply systematic problem-solving techniques for marketing challenges including campaign complexity (simplification cascades), creative blocks (collision-zone thinking), recurring campaign patterns (meta-pattern recognition), assumption constraints (inversion exercise), audience scale uncertainty (scale game), and dispatch when stuck. Techniques derived from proven problem-solving frameworks adapted for marketing execution.
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Codified expertise for returns authorization, receipt and inspection, disposition decisions, refund processing, fraud detection, and warranty claims management. Informed by returns operations managers with 15+ years experience. Includes grading frameworks, disposition economics, fraud pattern recognition, and vendor recovery processes. Use when handling product returns, reverse logistics, refund decisions, return fraud detection, or warranty claims.
日本語の概要は準備中です。原文の説明を表示しています。
Identifies audio content using Chromaprint/AcoustID fingerprinting, Shazam API recognition, and ACRCloud monitoring. Matches music, speech, and ambient audio against fingerprint databases.
日本語の概要は準備中です。原文の説明を表示しています。
通过元认知三阶段控制大脑信息处理流程。当你"知道该做什么但做不到"、情绪被绑架后无法理性思考、或需要系统性提高大脑信息处理质量时使用。三阶段：控制输入（注意力把关信息入口）→控制大脑（对信息丢弃/储存/处理）→控制输出（将思考结果高质量执行）。不适用于需要快速本能反应的紧急场景。关键触发信号："知道但做不到""我又情绪化了""脑子很乱""学了但用不出来"。
日本語の概要は準備中です。原文の説明を表示しています。
Reference for advanced data structure patterns used in competitive programming and technical interviews. Covers heaps, monotonic stacks, tries, segment trees, Fenwick trees, stack-based parsing, and ordered sets with Python templates, recognition signals, and edge case guidance.
日本語の概要は準備中です。原文の説明を表示しています。