Write early-alert outreach messages for at-risk college students
Drafts supportive, specific outreach to students flagged for missing class, low grades, or disengagement.
The prompt
Help me write outreach to college students flagged by our early-alert system.
Alert reasons: [e.g. "missed 3+ classes", "below C at midterm", "not logged into LMS for 10 days", "registration hold", "missing FAFSA"]
Student population: [e.g. first-year, first-gen, online adult learners, athletes]
Who is sending: [advisor, instructor, success coach]
Resources to point to: [tutoring, counseling, emergency aid, food pantry, disability services]
Key dates: [withdrawal deadline, incomplete deadline, registration opens]
Channels: [email, text, LMS message, phone]
For each alert reason, write:
1. An email (under 150 words) that names the specific concern without shaming, opens with care, and offers one easy next step (book a 15-minute meeting, reply with a time).
2. A text message (under 300 characters).
3. A second-touch message if there is no reply in 5 days.
4. A phone script for a third attempt.
Rules:
- Assume the student may be dealing with work, family, money, or health issues; ask, do not presume.
- Put deadlines in bold and explain consequences of missing them plainly, such as the effect on financial aid of withdrawing.
- Keep it FERPA-safe: no grade details over text or to anyone other than the student.
- Include a crisis line for messages mentioning wellbeing, e.g. 988 in the US.
Also give a short tracking table to log contact attempts and outcomes.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Email & Workplace Communication prompts
What to fill in
- e.g. first-year, first-gen, online adult learners, athletes
- advisor, instructor, success coach
- withdrawal deadline, incomplete deadline, registration opens
- email, text, LMS message, phone
How this prompt is built
This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.
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