[Scenario]Education & Research Scenarios: AI for Papers, Courseware & Data Processing

Summary: Let AI be your paper assistant, courseware generator, and data processing aide.

1. The Three Mountains Every Researcher Knows

Are you a graduate student, a university instructor, or an engineer at a research lab? If your work touches "education and research," these three breakdowns will feel painfully familiar:

1. The literature review that fries your brain. Your supervisor drops a line: "Give me a synthesis of progress in field A over the last five years." You open CNKI and PubMed, hunt down 200 PDFs, and by paper 30 your eyes start drifting—papers 31 through 200 hover somewhere between "I've read it" and "I haven't."
2. The courseware you rebuild every semester. It's your third time teaching the same course, and you still rebuild the deck from scratch each term—chopping the textbook into knowledge points, dreaming up cases, designing interactive segments. A 45-minute lecture deck takes at least half a day.
3. The Excel graveyard of experimental data. You've finished three sets of controlled experiments, and your spreadsheet holds hundreds of rows. Your advisor asks for "basic statistics and a chart." The formulas are hazy, SPSS isn't your friend, and Origin still needs installing. You end up screenshotting the data and pleading for help over WeChat.

These three mountains—reading literature, building courseware, and crunching data—are precisely AI's strengths: it can ingest documents, produce structured outputs, and generate code. Today we'll walk through three real-world mini-cases and knock them down one by one.

2. What You'll Walk Away With Today

After this section, you'll have these three deliverables in your hands within 30 minutes:

• Deliverable 1: A ~1,500-word first-draft literature review plus a reference list formatted in GB/T 7714 (China's recommended citation format, which looks like this: [1] Author. Title[J]. Journal, Year, Volume(Issue): Start–End pages.).
• Deliverable 2: A 12–15-slide PPT outline (each slide includes a title, knowledge points, a case, and an interaction prompt).
• Deliverable 3: A "mean ± standard deviation" summary of your experiment data (a multi-group comparison table) plus an appropriate statistical chart (bar chart or box plot).

A quick note: these are all first-draft / sketch-level artifacts, not final versions. That's a basic rule of AI-augmented office work—AI gets you 80% of the way; the remaining 20% of professional judgment still has to come from you.

3. Hands-On: Three Scenarios Step by Step

Case 1: AI as Your Paper Assistant—From 200 PDFs to a 1,500-Word Review

Step 1: Feed the literature to AI.

Start by picking 5–10 papers that are tightly tied to your topic (a good mix: 5 review papers + 5 recent top-journal articles). Export their abstracts via CNKI or Google Scholar, or simply drop the PDFs into a long-context AI (Kimi, Zhipu Qingyan, Qwen, or ChatGPT all work). Feeding too many papers at once makes the model "lose focus."

Step 2: Write a structured prompt.

Don't just say "write me a literature review"—you'll get something bland. Try this template:

You are a graduate student in [field, e.g., Computer Vision / Education / Materials Science] writing a literature review. Based on the [N] abstracts I'm about to provide, extract 3 main research threads + 5 commonly acknowledged challenges + 2 future directions. Summarize each thread in 1–2 sentences. Output in English. Do not fabricate facts I haven't provided—if a paper doesn't cover something, write "Not addressed in the source."

Paste in the abstracts, and AI will give you a "skeleton-level" review.

Step 3: Format the references per GB/T 7714.

Then send:

Format the following references per GB/T 7714-2015. Output as plain text. Keep titles in their original language.

GB/T 7714 is the most common citation format in Chinese academic circles. It appears so often in AI training data that the model usually gets it right on the first try—but you still need to double-check author name romanization, journal abbreviations, and volume/issue/page numbers one by one.

Step 4 (a hard boundary!): Self-check + AIGC detection.

• Compare AI's "summaries" against the original papers sentence by sentence—AI "hallucinates," meaning it confidently makes things up.
• Run the text through your school's AIGC detection tool (many universities have already integrated "CNKI AIGC Detection" or "VIP AIGC Detection").
• An essential principle: This is an AI assistant, not a ghostwriter. Final outputs must be understood by and attributed to the human author. This is both the red line of academic integrity and the reason your defense committee can expose the gaps in your understanding with a single follow-up question.

Case 2: AI as Your Courseware Generator—Turn Textbook Chapters into a PPT Outline

Step 1: Drop the chapter text into AI.

Open Chapter X of Textbook Title, select and copy the whole chapter (subheadings are a bonus). Paste into the AI chat box. If the textbook is a PDF and you have copyright concerns, convert it first via screenshots + an OCR tool (OCR = converting text in images into editable text). For OCR you can use Qwen Reading Light (千问读光), Tencent Cloud OCR, and similar tools, depending on each platform's current offerings.

Step 2: Write a four-element prompt—role + audience + duration + structure.

You are a university instructor with 10 years' experience in [discipline]. The audience is second-year undergraduates, and this class runs 45 minutes. Please break the following textbook content into a 12–15-slide PPT outline. Each slide must include: ① a one-line title; ② 3 key knowledge points; ③ 1 real-life or engineering case; ④ 1 question or interactive segment. End with 1 "Summary" slide and 1 "Discussion Questions" slide.

Step 3: Once you have the outline, keep asking follow-ups.

Don't stop there. Once you have the outline, keep going:

Please expand slide 5 into a 3-paragraph speaking script, each paragraph no longer than 80 words, in a conversational tone so I can read it directly. For slide 7's "case," I want it tied to our [company / hometown / research group]. Please give me 3 alternatives.

Layer by layer, the courseware's "flesh" fills out. For visual layout, imagery, and animation, we still recommend doing it yourself in PowerPoint or WPS—AI-generated PPT visuals are still mediocre today. "AI for the outline; you for the layout" remains the best value-for-money combination.

Case 3: AI as Your Data Cruncher—Turn Excel Data into a Statistical Chart in Seconds

Step 1: Organize the data into a Markdown table.

Say you have 4 groups in a controlled experiment, each with 6 replicates. Organize the data in Excel like this (Markdown table = a simple table drawn with | and -, which AI loves):

Group	Replicate 1	Replicate 2	Replicate 3	Replicate 4	Replicate 5	Replicate 6
Control	12.3	11.8	12.5	12.0	11.9	12.2
Experiment A	14.1	13.8	14.5	14.2	13.9	14.3
Experiment B	15.6	15.2	15.8	15.5	15.3	15.7
Experiment C	13.2	12.9	13.4	13.1	12.8	13.3

Step 2: Ask AI to recommend a statistical method + write the code.

I have 4-group controlled experiment data as shown above (Markdown table). Please: ① give the mean ± standard deviation for each group; ② recommend the most appropriate statistical test and explain why; ③ recommend the most appropriate visualization (bar chart or box plot?); ④ give me directly runnable Python + matplotlib code, including error bars (the "little I-beam" on top of each bar).

AI will usually reply with something like "I recommend one-way ANOVA"—which happens to be the standard approach for 4-group comparisons.

Step 3: Run the code and produce the chart.

Paste the code into VSCode or Jupyter Notebook and run it; a PNG pops out. Reminder: before running, you must verify the code doesn't contain any dangerous operations like rm -rf—this rule applies to every "AI writes code" scenario.

Step 4 (more advanced): Embed the chart into your paper or PPT.

Drop the PNG into Word or PPT—and don't forget the figure number, caption, and data source. AI usually won't fine-tune the readability of scientific figures for you (axis labels, units, font size). Those details still need your hands.

4. Why AI Can Handle These Tasks

If you peel back the "AI capabilities" behind these three cases, they boil down to three moves:

1. Long-document understanding + summarization (Case 1): AI compresses tens of thousands of words of PDF into a few hundred words of essentials—core logic is "read—grab the key—reassemble."
2. Structured output (Case 2): You force AI to fill a fixed template like "title / knowledge points / case / interaction"—essentially asking AI to fill in a form you've already drawn.
3. Code generation (Case 3): AI translates your "human-language requirement" into Python, R, or SQL code. At heart, it's a programming translator.

A step further: RAG (Retrieval-Augmented Generation) lets AI "search your library first" (e.g., your Zotero library, textbook PDFs, or enterprise knowledge base) before answering, which dramatically reduces hallucinations. Zotero is the most popular reference manager for researchers (an enhanced EndNote, in essence) and supports AI plugins for local RAG. Mendeley is a comparable alternative—pick whichever fits your workflow.

One-sentence takeaway: AI is a "speaks-human, works-fast" intern, but it doesn't take responsibility for you—academic integrity, professional judgment, and final authorship always rest with you.

5. Five Red Lines: Pitfalls to Avoid in Education & Research

1. [Pitfall]Submitting AI's review as the final draft. AIGC detection will flag it instantly, and one follow-up question at your defense—"How did you come up with this paragraph?"—will trip you up.
2. [Pitfall]Letting AI cite papers it fabricated. AI will confidently invent non-existent authors, journals, and years. Verify every citation in Google Scholar or CNKI before using it.
3. [Pitfall]Skipping domain-knowledge review in courseware. AI occasionally gets formulas or concepts wrong (especially in cross-disciplinary or niche fields). Read everything yourself before class.
4. [Pitfall]Running unverified code on real data. AI-written Python may reference the wrong library or pass wrong parameters, producing a chart that looks "pretty but wrong." Validate on a small dataset first, then scale up.
5. [Pitfall]Uploading raw experimental data to public AI services. If your data involves unpublished findings, clinical data, or trade secrets, do not paste it into public AI chat boxes. Use a locally deployed model, or at minimum, anonymize the data first.

Compliance note: Many journals (the Nature and Science families) and universities now explicitly require "AI usage disclosure"—if you used AI, write one line in your Methods or Acknowledgements, e.g., "ChatGPT was used in the literature-organizing stage of this study." This is no longer a suggestion—it's increasingly a hard requirement. Always check the target journal's latest policy before submitting.

6. Level Up: Scale This Workflow 10×

Once the basics feel natural, level up in these directions:

• Paper Advanced: Link Zotero with a GPT plugin to auto-generate summary cards for each PDF; let AI help you draft a point-by-point rebuttal to reviewers (with the original text attached for comparison).
• Courseware Advanced: Let AI generate diagrams (illustrations / cover images), produce mind maps (Xmind + AI plugins), create AI screen recordings or AI avatar explanations.
• Data Advanced: Use Power Query for data cleaning (Power Query is Excel's built-in data-wrangling tool, enabling no-code merge, filter, and pivot); use DAX for metrics (DAX is the formula language of Power BI, used inside Power Pivot tables to define measures). For paper-based or scanned experimental records, run them through OCR first to get editable text.
• Cross-scenario Integration: Rewrite the figures from your paper into "one-sentence summaries" and drop them into your PPT—one source of content, three places it lives, double the efficiency.

Pick the most painful direction and dig deeper next: if you write papers, start with "Zotero + AI"; if you build courseware, start with "AI for outline + you for layout"; if you run experiments, start with "AI writes Python for your stats." See you in the next article.