AI Topic

How to Use This Plan #

Schedule: 12 weeks, 6 study days per week, about 30 minutes per day. Day 01–72 are study days, not calendar dates. Use the seventh day of each week for rest or catching up.
Goal: Explain AI concepts, summarize information, and discuss trade-offs in English at a B2 target level. These resources do not have official CEFR ratings.
Daily routine: 3 minutes of recall, 10 minutes of video study, 8 minutes of reading, and 9 minutes of speaking or writing. Save three useful phrases, not a long list of isolated words.
Manage the workload: Study a 3–6-minute video segment and roughly 300–500 words of the article. There is no need to finish a long video or article in one sitting. Focus labels below describe what to look for, not exact chapter titles or timestamps.
Repeat deliberately: Each week reuses one video/article pair. Listen for the main idea, check details with English captions when available, imitate a short passage, explain the idea, then review without notes.
Adapt the difficulty: If a segment is too difficult, shorten it to 1–2 minutes and use captions or a slower playback speed. Weeks 4 and 11 may require this extra support.
Sources: Videos are from IBM Technology/IBM Think, except Week 4 from 3Blue1Brown. Articles are from IBM Think. They are useful language material but reflect a vendor perspective; separate educational explanations from product promotion. Video links lead to individual YouTube videos or IBM pages with embedded videos; playback and caption availability depend on your region and browser.

Week 1 — AI, Machine Learning, and Deep Learning #

Video: AI, Machine Learning, Deep Learning and Generative AI Explained
Article: What is artificial intelligence (AI)?

Day	Topic	Video	Article	English Practice
01	What is AI?	AI explained — listen for the main idea	Artificial intelligence — introduction	Record a 60-second baseline explanation without a script.
02	AI vs. machine learning	AI explained — compare the definitions	Artificial intelligence — AI and machine learning	Write three comparison sentences using “whereas” or “while.”
03	Machine learning vs. deep learning	AI explained — replay the comparison	Artificial intelligence — deep learning	Explain the relationship using one everyday analogy.
04	Where does generative AI fit?	AI explained — focus on generative AI	Artificial intelligence — generative AI	Draw a concept map and describe it aloud for 90 seconds.
05	AI in everyday work	AI explained — replay an example	Artificial intelligence — applications	Write 80–100 words about one useful workplace application.
06	Review: explain AI to a colleague	AI explained — replay without captions	Artificial intelligence — check your definitions	Give a two-minute explanation; compare it with Day 01.

Week 2 — Training and Inference #

Video: AI Inference: The Secret to AI’s Superpowers
Article: What is AI inference?

Day	Topic	Video	Article	English Practice
07	What happens when a model answers?	AI inference — first listen	AI inference — definition	Describe inference in three plain-English sentences.
08	Training vs. inference	AI inference — listen for differences	AI inference — training comparison	Make a two-column comparison and explain it aloud.
09	From input to prediction	AI inference — follow the process	AI inference — how inference works	Use “first,” “next,” and “finally” to explain the process.
10	Why response time matters	AI inference — replay a technical passage	AI inference — latency and performance	Explain latency to a nontechnical colleague in 60 seconds.
11	Quality, speed, and cost	AI inference — revisit the explanation	AI inference — efficiency and deployment	Write 100 words about a trade-off using “however” and “depends on.”
12	Review: explain an AI request	AI inference — listen without captions	AI inference — verify your summary	Explain training and inference in two minutes without notes.

Week 3 — Generative AI #

Video: Generative models explained
Article: What is generative AI?

Day	Topic	Video	Article	English Practice
13	What does generative AI generate?	Generative models — first listen	Generative AI — introduction	Give three examples of generated content.
14	Generating vs. classifying	Generative models — compare tasks	Generative AI — generative and discriminative models	Explain the contrast using “unlike” and “in contrast.”
15	What is a foundation model?	Generative models — listen for model terminology	Generative AI — foundation models	Define three key terms in your own words.
16	How prompts shape an answer	Generative models — replay a model example	Generative AI — prompts and model outputs	Draft two versions of an instruction: vague and specific.
17	Useful applications and limitations	Generative models — revisit the applications	Generative AI — benefits and challenges	Write 100 words with one benefit, one limitation, and one example.
18	Review: propose a realistic use case	Generative models — replay without captions	Generative AI — fact-check your proposal	Give a two-minute proposal and answer one skeptical question.

Week 4 — Large Language Models #

Video: Large Language Models explained briefly — 3Blue1Brown
Article: What are large language models (LLMs)?

Day	Topic	Video	Article	English Practice
19	What is an LLM?	LLMs explained briefly — opening explanation	Large language models — definition	Explain an LLM without using the word “intelligent.”
20	Tokens and next-token prediction	LLMs explained briefly — prediction example	Large language models — how LLMs work	Explain “token” and “prediction” using a short sentence as an example.
21	Learning from text	LLMs explained briefly — training explanation	Large language models — training	Write five sentences using “is trained,” “is used,” and “is generated.”
22	Context and attention	LLMs explained briefly — transformer explanation	Large language models — transformers and attention	Give a simple explanation; identify one detail you still do not understand.
23	Capabilities do not guarantee reliability	LLMs explained briefly — replay the overview	Large language models — uses and limitations	Write 100–120 words using “can,” “may,” and “does not necessarily.”
24	Review: how does a chatbot work?	LLMs explained briefly — selected segment without captions	Large language models — verify terminology	Record a two-minute explanation and reuse five phrases from Weeks 1–4.

Week 5 — Hallucinations and Fact-Checking #

Video: Tuning Your AI Model to Reduce Hallucinations
Article: What are AI hallucinations?

Day	Topic	Video	Article	English Practice
25	What is an AI hallucination?	Reducing hallucinations — first listen	AI hallucinations — definition	Describe a hypothetical incorrect AI answer and why it matters.
26	Why can an answer sound convincing?	Reducing hallucinations — listen for explanations	AI hallucinations — causes	Explain a cause and effect using “because” and “as a result.”
27	Confident language vs. reliable evidence	Reducing hallucinations — replay a key claim	AI hallucinations — examples and risks	Rewrite three overconfident claims using cautious language.
28	Reducing errors	Reducing hallucinations — focus on mitigation	AI hallucinations — prevention approaches	Explain two ways to reduce errors without promising to eliminate them.
29	Checking an AI-generated answer	Reducing hallucinations — revisit the advice	AI hallucinations — detection and verification	Write a five-step fact-checking checklist in English.
30	Review: when should we trust AI?	Reducing hallucinations — replay without captions	AI hallucinations — verify your conclusion	Speak for two minutes using “The evidence suggests…” and “I would verify…”.

Week 6 — Retrieval-Augmented Generation #

Video: What is Retrieval-Augmented Generation (RAG)?
Article: What is retrieval augmented generation (RAG)?

Day	Topic	Video	Article	English Practice
31	Why retrieve information before answering?	RAG explained — first listen	Retrieval-augmented generation — introduction	Explain the problem RAG is intended to address.
32	Retrieve, add context, generate	RAG explained — follow the workflow	Retrieval-augmented generation — how RAG works	Describe the workflow in four ordered sentences.
33	External knowledge and freshness	RAG explained — external-information example	Retrieval-augmented generation — knowledge sources	Explain why a company might connect internal documentation.
34	Citations and verification	RAG explained — source-related explanation	Retrieval-augmented generation — benefits	Explain why a citation helps checking but does not guarantee correctness.
35	RAG vs. fine-tuning	RAG explained — revisit the RAG workflow	Retrieval-augmented generation — fine-tuning comparison	Write a 120-word comparison using “whereas” and “depending on.”
36	Review: design a documentation assistant	RAG explained — replay without captions	Retrieval-augmented generation — check your design	Present the data source, workflow, and one limitation in two minutes.

Week 7 — AI Agents #

Video: What are AI Agents?
Article: What are AI agents?

Day	Topic	Video	Article	English Practice
37	What makes a system an agent?	AI agents — first listen	AI agents — definition	Explain an agent with one practical example.
38	Goals, planning, and actions	AI agents — follow an example	AI agents — how agents work	Describe a task using “in order to” and “so that.”
39	Tools and external systems	AI agents — listen for tool use	AI agents — tool integration	Explain what a calendar or search tool adds to an agent.
40	Feedback and iteration	AI agents — replay the process	AI agents — reasoning and iteration	Describe an unsuccessful action and the next step using conditionals.
41	Autonomy and human oversight	AI agents — revisit the example critically	AI agents — risks and challenges	Write three rules about actions that require human approval.
42	Review: agent or chatbot?	AI agents — replay without captions	AI agents — check the distinctions	Compare two hypothetical systems in a two-minute explanation.

Week 8 — AI-Assisted Coding #

Video: What is an AI Code Generator? LLM Coding, Productivity, & Risk
Article: What is AI code generation?

Day	Topic	Video	Article	English Practice
43	How can AI assist a developer?	AI code generators — first listen	AI code generation — definition and uses	Describe three development tasks AI can assist with.
44	Giving clear coding instructions	AI code generators — replay an example	AI code generation — natural-language inputs	Draft a prompt with a goal, constraints, and acceptance criteria.
45	Reading and explaining generated code	AI code generators — focus on the workflow	AI code generation — code-assistance capabilities	Explain a familiar function in five plain-English sentences.
46	Testing before trusting	AI code generators — listen for risks	AI code generation — limitations	Explain why a plausible solution still needs tests and review.
47	Security and maintainability	AI code generators — revisit the risk discussion	AI code generation — challenges	Write a 120-word recommendation for responsible team use.
48	Review: should a team adopt an AI coding tool?	AI code generators — selected segment without captions	AI code generation — fact-check your recommendation	Give a two-minute recommendation with a benefit, risk, and safeguard.

Week 9 — Productivity and Automation #

Video: Reimagine business productivity with AI agents and assistants
Article: What is intelligent automation?

Day	Topic	Video	Article	English Practice
49	What work can AI help with?	AI and productivity — first listen	Intelligent automation — introduction	Describe a repetitive task from your own work.
50	Assistants, agents, and automation	AI and productivity — compare agents and assistants	Intelligent automation — automation components	Explain how these concepts overlap without treating them as identical.
51	Mapping a workflow	AI and productivity — replay a use case	Intelligent automation — applications	Describe a five-step workflow and identify one possible AI-assisted step.
52	Measuring useful improvements	AI and productivity — evaluate productivity claims	Intelligent automation — benefits	Propose two measures, such as task time and error rate.
53	What should remain under human control?	AI and productivity — revisit the use case	Intelligent automation — challenges	Write 120 words about a task you would not fully automate.
54	Review: propose a small pilot	AI and productivity — replay a segment without captions	Intelligent automation — verify your proposal	Present a workflow, success measure, and human checkpoint in two minutes.

Week 10 — Privacy and AI Governance #

Video: What is AI governance?
Article: Exploring privacy issues in the age of AI

Day	Topic	Video	Article	English Practice
55	Why do AI systems need rules?	AI governance — first listen	AI privacy — introduction	Explain the purpose of governance and give one privacy example.
56	Personal and confidential information	AI governance — replay the safety analogy	AI privacy — data collection and privacy risks	Describe three types of information you would avoid uploading.
57	Consent and transparency	AI governance — listen for responsibility	AI privacy — consent and data use	Draft three questions to ask an AI service provider.
58	Reducing unnecessary exposure	AI governance — revisit the control idea	AI privacy — privacy protections	Write five practical rules using “must,” “should,” and “must not.”
59	Accountability when something goes wrong	AI governance — replay the explanation	AI privacy — risks and safeguards	Explain who should investigate, communicate, and approve corrective action.
60	Review: a responsible-use policy	AI governance — replay without captions	AI privacy — check your policy	Present a two-minute policy covering data, oversight, and reporting.

Week 11 — Evaluating AI Systems #

Video: LLM as a Judge: Scaling AI Evaluation Strategies
Article: LLM evaluation: Why testing AI models matters

Day	Topic	Video	Article	English Practice
61	What does “good output” mean?	LLM as a judge — opening explanation	LLM evaluation — introduction	Define quality for one use case with three criteria.
62	Human and automated evaluation	LLM as a judge — compare approaches	LLM evaluation — evaluation methods	Explain one strength and one weakness of each approach.
63	Asking a model to judge answers	LLM as a judge — follow the judging process	LLM evaluation — model-based evaluation	Describe the process using “according to the criteria.”
64	Rubrics and test cases	LLM as a judge — replay a practical passage	LLM evaluation — evaluation criteria	Write a three-point rubric for judging an English summary.
65	Bias and unreliable scores	LLM as a judge — listen for limitations	LLM evaluation — evaluation challenges	Write 120–150 words explaining why one score is not enough.
66	Review: evaluate two sample answers	LLM as a judge — selected segment without captions	LLM evaluation — check your criteria	Draft two short answers yourself and justify their scores aloud.

Week 12 — Open Models and Choosing an AI Approach #

Video: Should you use open source Large Language Models?
Article: What is open-source AI?

Day	Topic	Video	Article	English Practice
67	Open and proprietary models	Open-source LLMs — first listen	Open-source AI — introduction	Explain the contrast without assuming that “open” means unrestricted.
68	Open source, open weights, and licenses	Open-source LLMs — listen for openness claims	Open-source AI — definitions and licensing	Explain why a model’s license and available artifacts both matter.
69	Control, customization, and deployment	Open-source LLMs — focus on benefits	Open-source AI — customization and transparency	Describe a scenario where local deployment could be useful.
70	Costs and shared risks	Open-source LLMs — replay the trade-offs	Open-source AI — costs and risks	Compare two approaches using quality, privacy, cost, and maintenance.
71	Preparing a recommendation	Open-source LLMs — revisit the main argument	Open-source AI — evidence for your decision	Write a 150-word recommendation using ideas from at least three weeks.
72	Final presentation and reflection	Open-source LLMs — replay a segment without captions	Open-source AI — final fact-check	Give a three-minute presentation; compare your clarity with Day 01.

Weekly Self-Check #

After each sixth study day, record:

Listening: Can I identify the main point and three supporting details without captions?
Vocabulary: Can I use five useful phrases from this week in new sentences?
Speaking: Can I explain the topic for two minutes using only a few keywords?
Writing: Can I produce a short summary with a main point, an example, and a limitation?
Next step: Which one difficulty should I focus on next week? Repeat a difficult week if needed; finishing on schedule is not a language-proficiency test.