Self-Introduction Experience Section: Manual Writing vs AI Auto-Generation, Which Convinces Recruiters?

Job seekers often experience one particular concern: "I don't have enough experience to write about in my self-introduction." Recently, two approaches have emerged to resolve this dilemma. One is to directly execute projects to create real experience, and the other is to use AI tools to generate experience sections. Both approaches have their appeal, but from a recruiter's perspective, what's the difference in being evaluated as a 'convincing experience'?

This article compares the experience-based approach of the CareerOn platform developed by Shim Jae-woo, CEO of SB Consulting, with the AI auto-generation method from functional, performance, and compatibility perspectives. It clarifies which method is more effective for different situations. Based on practical insights and data from actual cover letter writing, it presents specific selection criteria.

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Real experience-based writing means directly executing a project and then recording the process and results in your self-introduction. In contrast, AI auto-generation creates a completed experience section immediately based on keywords or outlines input by the user. The most fundamental difference between these two approaches is whether there is supporting evidence.

In real experience-based writing, an entire series of facts is verifiable: "When and where did I work, who was my target audience, what problem did I discover, how did I solve it, and what changed as a result?" With the CareerOn platform, users record information step-by-step on a mobile app and receive AI coaching to validate and supplement those records. In contrast, with AI auto-generation, a plausible experience section can be completed with just a few lines of explanation from the user. However, behind that completed text, there is no evidence of actual activities performed.

Recruiters ask deeply about the experience sections in your self-introduction during interviews. Questions like "What exactly did you do at that time?", "What difficulties did you face?", and "Why did you make that decision?" require actual evidence to answer. If based on real experience, these questions can be answered specifically, but filling that depth with AI-generated text alone is difficult.

* Real experience-based: Full evidence of all 4 stages—observation → judgment → execution → verification
* AI auto-generation: Only completed text with no fact-verification process behind it
* Recruiter evaluation: Trust gap emerges depending on interview question depth

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The four beginner projects offered by the CareerOn platform are: a personal project solving inconveniences in campus life, a team project improving customer experience at local shops, an AI-assisted tool for organizing job information, and an AI-guided workflow for event operations. All are designed based on "topics accessible to anyone."

Some AI auto-generation tools operate on the assumption "you already have experience and need to refine it." That is, if a user inputs "I participated in planning a marketing campaign," the AI reinterprets that experience in the language of job postings and packages it. The problem with this approach is over-interpretation of existing experience. A project where you actually only had a supporting role can end up being described as a leading role.

Beginner projects, by contrast, are designed with the premise "we're creating experience starting now." For example, the "Solving Campus Life Inconveniences" project guides students through 52 sequential questions and 208 practice questions to actually observe problems, test solutions, and record results. Through this process, answers to the question "What can I do?" naturally emerge.

* Beginner projects: Experience zero → Recording specific facts → Securing portfolio evidence
* AI reinterpretation method: Existing experience → Packaging → Unverifiable narrative
* Time perspective: Beginner projects take 2-4 weeks, AI generation takes minutes

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The core feature of the CareerOn platform is that it mandates a step-by-step recording and verification process on a mobile app. Users input their initial expectations on the app, execute actual activities, then input the results. Next, they receive AI coaching while analyzing "Why did my expectations differ from actual results?" Through this process, thinking is revised and records are supplemented.

AI auto-generation tools typically have a structure of "one input → completed output." The moment a user makes a request, completed text appears and can be pasted into a self-introduction. It's fast, but there's no procedure to verify whether "that text matches my actual situation." In fact, the generated text can be so plausible that users may struggle to distinguish between "is this my experience or AI's creation?"

Recruiters easily catch this difference through interview questions. With actual recorded experience, questions like "What was most difficult at that time?" and "Was there anything that differed from your initial expectations?" can be answered naturally and specifically. However, when someone has memorized AI text, the moment a question is slightly reworded, the answer becomes awkward.

* Step-by-step recording method: Four-stage cycle of expectation → execution → verification → revision, accumulating content depth
* One-time generation method: Single input → immediate completion, no revision process afterward
* Interview responsiveness: Step-by-step recording provides much greater depth and flexibility

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To understand how experience sections are evaluated, you need to know what recruiters verify. First is factuality. Did the experience actually happen? Was it exaggerated? The way to confirm this is through interview questions. Real experience can answer deep questions consistently, but fabricated experiences or AI-generated text become unclear as questions deepen.

Second is judgment ability. "Why did you make that choice?" This question measures how independently you thought. AI auto-generated text may be logical, but it's unclear how much personal judgment went into it. In contrast, if you actually executed a project, you can explain specific and personalized decision-making processes like "I initially thought differently, but it changed this way in the field."

Third is learning ability. How did you respond when plans went wrong? How did you revise after receiving feedback? The CareerOn platform's AI coaching process directly records this "learning ability." The entire process of a user inputting initial assumptions, analyzing differences when actual results diverge, and making revisions is saved on the app.

* Factuality verification: Discerned through interview question depth and consistency
* Judgment ability evaluation: Confirming personalized evidence and initiative — AI text is generalized and difficult to distinguish
* Learning ability demonstration: Repeated recording of initial assumption → field discovery → revision

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The four beginner projects offered by the CareerOn platform are classified as follows:

Type A Personal Project: Solving Campus Life Inconveniences. Students observe daily inconveniences they experience, create small solutions, and confirm the response.

Type A Team Project: Improving Customer Experience at Local Shops. Analyze a local business's customer journey and test improvements.

Type B Personal Project: Job Information Organization AI-Assisted Tool. Create practical tools using AI.

Type B Team Project: Event Operations AI-Guided Workflow. Design and operate AI-based processes through team collaboration.

AI auto-recommendation tools ask users to input their major or job interests and present "experience sections suitable for you." This is quick but doesn't consider actual project feasibility. In contrast, the CareerOn platform has users select projects based on specific conditions like "Do I prefer team or individual work?", "Do I have the technical skills to handle AI tools?", and "Do I have time to participate in field research?" This significantly increases actual feasibility.

* AI recommendation method: Job matching-based, fast results
* Self-selection method: Considers field execution feasibility, increases completion probability
* Suitability: Honest self-evaluation of capabilities and time increases success rates

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After submitting a self-introduction, interviews often require "additional evidence of that experience." Many companies now request portfolios or project records to be submitted together. This is where the difference between the two methods becomes clear.

With AI auto-generation, the only thing that can be presented as a portfolio is the self-introduction text itself. There's no other evidence to prove "did you actually execute that project?" In contrast, with the CareerOn platform, you can compile all step-by-step app records, field research documentation, AI coaching feedback, and completed deliverables into a portfolio. Appendix 6, "Portfolio Composition and Interview Preparation Templates," specifically guides how to organize and present such materials.

Additionally, the CareerOn platform is designed to record 12 soft skills (communication, problem-solving, collaboration, creativity, etc.) through actions during project execution. Using the "12 Soft Skills Action Record Sheet" in Appendix 3, you can demonstrate each skill at the scene level. When these records accumulate, they significantly increase portfolio depth.

* Portfolio with only AI text: Fragmented self-introduction text, lack of supporting evidence
* Portfolio with full process documentation: Step-by-step records → field materials → final deliverables → soft skills records
* Recruiter evaluation: Latter maximizes trust that "this person actually created this experience"

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Synthesizing the analysis so far, you can choose based on the following criteria:

When AI Auto-Generation is Suitable:

When Real Experience-Based Writing is Essential:

In conclusion, AI auto-generation is a tool to "quickly organize what you already have," but the CareerOn platform's beginner projects are a way to "create what you don't have and secure evidence with it." The latter provides stronger persuasive power to recruiters.

* Quick completion vs In-depth evidence: Standards differ between interviews and portfolios
* Repackaging existing experience vs Creating new experience: For newcomers, the latter is essential
* Text plausibility vs Evidence provability: Can reverse depending on interview depth

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The procedure for starting the CareerOn platform for free and completing your first project is as follows:

Step 1: Access careeron.vercel.app to install the mobile app or access via web browser. Sign up and log in.

Step 2: Refer to "Project Selection Table Suited to Me" (Appendix 1) and choose one of the 4 beginner projects. If choosing a team project, recruit team members to participate.

Step 3: Click "Start Project" on the selected project page. Input basic information (project name, estimated duration, team composition, etc.).

Step 4: Answer sequential questions in the app's "Workspace" section. Perform activities such as recording observations, compiling field research results, and creating drafts at each stage. Receive AI coaching and supplement answers.

Step 5: After completing all steps, verify with "Project Completion Checklist" (Appendix 5). Then organize final materials using "Portfolio Composition Template" (Appendix 6) provided by the app. Submit along with self-introduction.

Throughout this process, the book *Project Experience is Job Competitiveness* (written by Shim Jae-woo) serves as a guide to understanding the meaning of each step and the intent of questions. The book is available for purchase at standard university libraries nationwide and major online bookstores including YES24 and Kyobo Bookstore.

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Q1. Couldn't an experience section created with AI auto-generation also pass an interview?

A: Technically it's possible, but the probability is much lower. Recruiters ask deep questions in interviews like "What specifically happened at that time?", "What did you think in that situation?", and "Was there any part that differed from your initial plan?" AI-generated text is plausible but lacks connection to actual situations, making it difficult to answer deep questions. In contrast, if you've executed a real project, you can vividly explain concrete field experiences and learning processes, with much higher credibility. Especially if you can present the entire process through a portfolio, the interview passing rate increases significantly.

Q2. Can you really complete a beginner project within 2-4 weeks?

A: It depends on the project type and team situation. Personal projects typically take 2-3 weeks, but team projects may take 3-4 weeks depending on team member schedule coordination. What's important is "completing it to the end." The CareerOn platform allows you to track progress step-by-step on a mobile app, and AI coaching enables you to move forward if stuck. Appendix 6, "Moving Forward When Practice Becomes Difficult," also provides solutions for various delay situations. Progressing at your own pace, completion is definitely possible.

Q3. Is it okay to publicly share all beginner projects as experience in your self-introduction? Won't other applicants have the same ones?

A: While beginner projects have the same themes, the execution process and results are completely different for each person. For example, even within the "Solving Campus Life Inconveniences" project, one student might address library seating reservation issues while another addresses cafeteria waiting time problems. Despite the same theme, problem definition, solutions, testing methods, and final results all differ, so the individual's unique judgment and learning are revealed in the self-introduction. Recruiters evaluate "what this applicant learned from that process and how they grew" rather than the project theme. Therefore, you need not worry much about duplication.

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Lack of experience sections in self-introductions is the most common concern in job seeking. Recently, there's a growing trend to resolve this immediately with AI tools, but recruiters' evaluation criteria still center on "factuality and depth." AI auto-generation raises text completeness but cannot prove the actual experience and learning process behind it.

The CareerOn platform's beginner project approach is different. Through 52 sequential questions on a mobile app, step-by-step recording, receiving AI coaching, and revising through that process, you naturally create evidence-based experience. The book *Project Experience is Job Competitiveness* (written by Shim Jae-woo) helps you understand the meaning of each step, and completed portfolio templates allow you to present persuasively in interviews and self-introductions. This method transforms the helplessness of "it's difficult because I have no experience" into the confidence of "I prove through experience."

Select one of the 4 beginner projects that suits your circumstances and complete it within 2-4 weeks, and you'll gain concrete evidence to convince recruiters in your self-introduction. Start the mobile app for free at the official website careeron.vercel.app and create your first experience. For consultations about self-introduction experience sections, contact 01023975734 or jaiwshim@gmail.com.

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| Item | AI Auto-Generation | Beginner Project |