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Week 9 · Readings & resources

Week 9 — Readings & Resources · The AI Tool Landscape — Choosing the Right Tool

Using Artificial Intelligence · AI 101 Fall 2026 · Prof. Quinn Fictional sample

Course: Using Artificial Intelligence (AI 101) · Silver Oak University (fictional sample) · Prof. Quinn
Objective covered: Objective 3 — Work across modalities and choose the right AI tool for a given job from the current tool landscape.

How to use this page

Everything here is a link to an external resource — open it in your browser. Nothing needs to be downloaded or installed.

This week's load is intentionally practical: official product homepages for each tool category (the most reliable source for what each tool does today), plus one or two accessible overview articles. Explore the homepages — this is the research the week is built around. Total time is roughly 30–40 minutes for the readings; the tool homepage exploration is open-ended and part of the Studio prep.

Order that matches the lecture: ① big-picture overview → ② chatbots → ③ image tools → ④ audio/music/voice → ⑤ video → ⑥ research tools → ⑦ coding assistants → ⑧ staying informed.

This week's special habit: when you visit each official homepage, ask: What specific job is this tool built for? What does it say it can't do? That question makes the exploration purposeful.

① The Big Picture — The AI Tool Landscape

Maps to Lecture Segments 1–2. Why categories matter more than individual product names.

Reading — "The Landscape of Generative AI Tools" (MIT Sloan Management Review)
🔗 https://sloanreview.mit.edu/article/the-landscape-of-generative-ai-tools/
Why it's assigned: a readable, non-technical overview of how the generative AI tool market has fragmented by use case — exactly the framing we built in class. Focus on the category distinctions, not the specific product names (those change fast).
⏱ ~10 min

② General-Purpose Chatbots / Assistants — Official Homepages

Maps to Lecture Segment 2, Category 1. These are your approved AI assistants for coursework.

Note: visit at least one of these you don't currently use and explore what it describes as its strengths. Notice whether it names tasks it's better at than competitors — and whether those claims are specific or vague.

③ Image Generation Tools — Official Homepages

Maps to Lecture Segment 2, Category 2. These generate images from text prompts — not the same as chatbots.

Explore the Midjourney homepage and the Firefly homepage. Notice the differences in how each describes its intended users and use cases.

④ Audio, Music & Voice Generation — Official Homepages

Maps to Lecture Segment 2, Category 3. Music generation (Udio, Suno) and voice synthesis (ElevenLabs) are different sub-categories.

Spend two minutes on Udio and two on ElevenLabs. The difference in what each describes as its output should be immediately clear — one makes music, the other makes speech. That distinction is quiz-material.

⑤ Video Generation — Official Homepage

Maps to Lecture Segment 2, Category 4. Video generation from text prompts.

Explore what Sora describes as its capabilities and limitations. Video generation is a rapidly developing area — note that the official page is the only reliable source for current status.

⑥ Research Assistants / Notebook Tools — Official Homepage

Maps to Lecture Segment 2, Category 5. Grounded in your own documents — fundamentally different from a general chatbot.

Read the NotebookLM homepage carefully. Notice how it describes its relationship to your uploaded sources — it specifically grounds answers in what you provide, not in general training. That's the key distinction from a chatbot.

⑦ Coding Assistants — Official Homepages

Maps to Lecture Segment 2, Category 6. Integrated in-editor coding tools are different from simply asking a chatbot to write code.

If you're not a programmer, still skim one of these. Notice how the tool describes its integration into a workflow (the editor, the codebase, the context it sees) — that's the distinction from "chatbot that writes code."

⑧ Staying Informed — Following the Tool Landscape

Maps to Lecture Segment 7. Durable habits for keeping up as tools change.

Reference — Release notes and official changelogs
Each major tool maintains an official blog or release-notes page. Rather than linking to any single one (they update continuously), the habit to build is: when you want to know what a tool can do right now, go to its official page — not a review site or a social media post.

A useful weekly scan:
The major AI company blogs are worth bookmarking:
- OpenAI blog 🔗 https://openai.com/news
- Anthropic blog 🔗 https://www.anthropic.com/news
- Google DeepMind blog 🔗 https://deepmind.google/discover/blog/
- xAI blog 🔗 https://x.ai/blog

Not required reading — but if you want to stay current on the tool landscape after this course, these are the authoritative sources. Anything you read elsewhere about these tools should be traceable back to one of these or a similar primary source.

Pick-one quick path (≈15 min total)

In a hurry? Do exactly these and you'll be ready for the quiz:
1. Read the MIT Sloan overview (group ①) — 10 min.
2. Spend 90 seconds each on the Suno homepage, the NotebookLM homepage, and the GitHub Copilot homepage — focus on what output each produces and what use case each describes.

Heads-up (links rot): these point to outside sites that can change their URLs or page structure. If a link ever fails, search for the tool's official homepage directly (a search for "Udio official site" or "NotebookLM Google" gets you there). Nothing here is downloaded or redistributed — all resources are links to their original sources.

~ Prof. Quinn's edition · Fall 2026 · built with thecoursemaker.com