✅ Inside this issue:

• Use this AI hack to make credential CSV uploads less painful

• Thania shares how to evaluate women on results, not perceptions

• The conference we think should be on your calendar next month

• Why "luck" has a lot less to do with success than you think

INTERESTING READS

🏛️ Why every state needs an LER officer as digital credentials move from pilot projects to public infrastructure.

🤖 Your team isn't ready for AI yet, but this 90-day plan can help change that.

🧠 Six practical ways to reduce anxiety and create a healthier workplace.

🏗️ A new whitepaper explores the infrastructure challenges standing between today's workforce systems and tomorrow's skills ecosystem.

PRODUCTIVITY

The time-saving AI hack for bulk uploading credentials

If you’ve ever managed a digital badging or credentialing platform, you already know the glamorous truth:

Sooner or later, everything becomes a spreadsheet.

Credly, Accredible, Parchment Digital Badges, Canvas Credentials, SmartResume, POK, internal talent systems, workforce databases — whatever platform you’re using, there is usually a moment where someone says, “Can you just upload the CSV?”

And suddenly…cue the panic because we all know your source material is almost never a clean CSV.

It’s a course catalog. A PDF. A credential framework. A syllabus. A Google Doc. A competency map. A 47-tab spreadsheet named something like “FINAL_final_USE_THIS_ONE_v6.”

And now someone has to turn that mess into the exact columns your credentialing platform requires.

That task has a name: field mapping. And AI can make it dramatically faster.

First, know what kind of upload you’re doing

Before we get cute with prompts, let’s clarify something important.

In many credentialing platforms, “bulk upload” usually means one of two things:

1. Credential setup data
   This is the metadata about the badge or credential itself: title, description, criteria, skills, competencies, tags, alignment, expiration rules, and related details.

2. Credential issuance data
   This is the recipient file used to award credentials to learners: recipient name, email, issue date, badge template ID, evidence, expiration date, and similar fields.

Those are not the same thing.

For example, Credly’s bulk issuing workflow requires issuers to download and use its CSV template, keep the headers intact, and populate required fields like badge template ID, recipient email, first name, last name, and issue date. Credly also notes that changing the template format, using incorrect headers, leaving required fields blank, entering invalid emails, or using the wrong date format can trigger bulk issuing errors.

Parchment Digital Badges also supports bulk awarding through CSV workflows, but the process is different. Users create the issuer and badge first, download a sample CSV, upload the file, map column headers, and validate the upload before issuing.

Accredible’s batch issuing workflow also uses spreadsheet uploads, with required recipient fields such as name, email, and issue date.

Basically, do not assume one platform’s CSV will work in another. Take note of each and you’ll avoid unnecessary spreadsheet chaos.

Step 1: Collect your source materials

Start by gathering the documents that contain the information you need.

This might include:

• course catalog

• badge metadata framework

• credential design document

• syllabus

• competency framework

• skills taxonomy

• program learning outcomes

• platform CSV template

• issuer naming conventions

The key is to give AI both sides of the puzzle: the messy source material and the destination template.

Step 2: Download the platform’s CSV template

Do not make your own from scratch if the platform gives you one.

Use the official template.

Platforms often require exact column headers, specific date formats, required fields, and hidden validation rules. Credly’s own guidance says to download a fresh template, keep the headers intact, and leave unused optional columns blank rather than deleting them.

This is why your first job is not “make a CSV.” Your first job is: understand the template.

Identify:

• required columns

• optional columns

• platform-specific fields

• accepted date formats

• character limits

• fields that require IDs

• fields that can stay blank

• fields that need human review

Step 3: Build a field map

This is the step most people skip. A field map tells you how your organization’s language translates into the platform’s language.

For example:

• “Course title” → credential name

• “Course code” → credential ID

• “Course description” → credential description

• “Learning outcomes” → criteria or skills

• “Competencies” → alignment

• “Program area” → tags or category

• “Student email” → recipient email

• “Completion date” → issue date

This is where AI is genuinely useful. You are asking it to translate messy information into a structured format.

Step 4: Ask AI to extract into a table first

Do not ask AI to generate the final CSV immediately.

Start with a preview table. Use a prompt like this:

I am preparing a bulk upload file for a digital credentialing platform. I will provide two things: 1) source materials from our course catalog, syllabus, or credential framework, and 2) the platform’s CSV template with exact column headers. First, create a field map showing which source fields map to each CSV column. Then extract the available data into a table using the platform’s exact column names. Do not invent missing information. If a required field is missing, mark it as “needs human input.”

This gives you a cleaner review stage before anything gets uploaded.

Step 5: QA a small sample

Before processing 300 records, test 3 to 5.

Check:

• Are required fields complete?

• Are names and emails formatted correctly?

• Are skills mapped consistently?

• Are descriptions too long?

• Are dates in the correct format?

• Are commas, symbols, or line breaks going to break the CSV?

• Did AI hallucinate anything?

If the sample is wrong, your full file will be wrong faster. And while speed is fun, fast garbage is still garbage.

Step 6: Generate the CSV

Once the sample looks right, ask AI to create the full table using the exact template columns.

Then export it through Excel or Google Sheets as a CSV.

Before uploading, validate it against the platform’s instructions. Many systems will flag missing fields or formatting issues during upload, but you want to catch as much as possible before you get there.

👉Steal the prompts here

Humans still required

AI doesn’t replace the human judgment needed to design a good credential and all of the steps before this but it can absolutely save you from spending six hours copying and pasting course descriptions into a spreadsheet while questioning your life choices.=!

Just remember: AI can help map, extract, and format.

Humans still need to verify, approve, and upload.

Small security note: Don’t upload private learner data, emails, grades, or sensitive institutional records into AI tools unless your organization has approved that use.

💙 Liked this article? Let the author know → Click here to give kudos to our editorial team.

CONFERENCES

Join us at Badge Summit

We're excited to share that both Robert and Thania will be presenting at Badge Summit in Boulder next month.

If you're attending, we'd love to see you there. Feel free to stop by one of our sessions, say hello in the hallway, or grab us between presentations.

📍 Monday, July 13 | 11:30 AM – 1:00 PM @ Ballroom
Badgesplaining: How to Explain LERs to Your Grandma with Thania Guardino

📍 Wednesday, July 15 | 10:15 AM – 10:45 AM @ UMC 285
Evaluating the Technical Foundations of Badge & LER Systems: A Ready-to-Use Self-Assessment Tool with Robert Bajor + Nate Otto

There's still time to attend if you haven't registered yet!
👉 Register for Badge Summit

WORKPLACE

How to conduct performance reviews based on performance, not personality (when it comes to women)

Gentlemen, this might come as a shock: 88% of high-performing women get reviewed on their personality, not their performance. For high-performing men, it's 12%.

To all my fellow high-achieving, assertive women who are well-versed in the personality-based performance review… 👋 hey girl, hey.

You're doing great, but you're too intimidating

One time, as an executive at a company I loved, my CEO told me I was crushing my goals. Then came the "but." We needed to talk about how I was "too intimidating" to some of the more junior female staff.

Being new to "these types" of reviews, I nodded and accepted the constructive criticism like a good girl.

Here's what actually happened. We were growing, with new offices in other states and people working remotely, and the perception of me had curdled into "mean and intimidating."

The source? An over-eager new hire had asked me to approve marketing material that wasn't up to brand guidelines. I said no, pointed her to the existing on-brand assets, and told her the marketing team would take it from there. She complained that I was blocking her resourcefulness and problem-solving.

So, to recap: I did my job (keep the brand on brand), and got labeled scary for it.

My CEO's fix? Fly me out to tour the new offices so everyone could meet me and see I wasn't so frightening in person.

Women are often held to unfair gender bias

Now, my male peers, frankly more direct, more cold, and never once called "friendly," didn't have this problem. Their reviews were about… you guessed it… their performance! Nobody had notes on their personality.

Don't get me wrong. There's real leadership value in building relationships across a growing team, and the office tour was a smart call. But it should have been framed as mentorship, an investment in those junior staffers. Instead it landed as a flaw of mine to "work on."

And this is the part I keep coming back to: I can't make anyone feel intimidated. Nobody can. That feeling lives in the room, not in my job performance.

So when I asked for one concrete example of the intimidating or mean behavior? My CEO had nothing. No incidents. No quotes. Just the subjective read of a few junior staffers.

Welcome to the complicated world of being a woman at work! Yay.

Okay. Lesson delivered. Now here's how you, yes you, the one writing the reviews, keep from making my CEO's mistake.

The playbook: review the work, not the woman

1. Write the scorecard before the season starts

Before the review window opens, write down what the role is actually supposed to produce: the goals, the projects, the numbers. Then hold every line of feedback to one rule: if it doesn't trace back to a goal or a deliverable, it doesn't go in the review. Stanford researchers found that tying reviews to performance, keeping the process transparent, and holding managers accountable is exactly what shrinks the room for stereotypes. Same scorecard, same way, for everyone.

2. Kill the blank comment box

That open "share your thoughts on this person" field is where bias throws a party. Blank boxes invite vibes, and vibes skew against women. Swap them for structured prompts: What did they ship this quarter? What was the impact? What's the next stretch goal? When the questions are about the work, the answers are too.

3. Make every adjective earn its place

"Intimidating." "Abrasive." "Too much." "A joy to work with." Those are subjective qualifiers, not feedback. Give each one a test: can you attach a specific moment and a business impact? If not, cut it. This matters more than it looks: women are eleven times more likely than men to be called "abrasive" in a review. (It's the one word men never see.) Make people show their work before they get to use the word.

4. If it's not actionable, it's not feedback

"Be less intimidating" tells me exactly nothing to do on Monday. Every critique needs a specific example and a concrete next step. Steal this:

The first is a verdict on her character. The second she can use Monday morning. And it costs you when you get it wrong: employees who receive vague, low-quality feedback are 63% more likely to quit within the year. Your best people feel the fog first.

5. Performance and development are two different talks

This is the one my CEO fumbled. Hitting your targets is performance. Coaching up junior staff and getting more approachable across offices is development. Smash them together and "here's a chance to grow your leadership reach" turns into "here's a defect to fix." Keep them in separate conversations, and frame growth as an investment in someone who's already winning.

6. Calibrate out loud, across genders

Before reviews go out, get managers in a room to compare the actual language they used. Are the women getting "collaborative" and "helpful" while the men get "strategic" and "ambitious"? Calibrating across managers, and across genders, is one of the most consistently recommended fixes there is. You don't need a task force. You need a second set of eyes and one question: would we write this about a man?

7. Audit the patterns once a year

Zoom out and read the reviews side by side. Track ratings, promotions, and pay by gender over time. The bias rarely lives in one ugly review. It lives in the aggregate: the slow drift of "nice" for her and "driven" for him. You can't fix a pattern you won't look at.

It’s really about focusing on the work

To be clear, none of this is about coaching women to act more like men, and it's not about scrubbing warmth out of the office. It comes down to one question: does the review measure the work?

And for those of us building toward a skills-based economy, there's a bigger picture. We love to say people should be valued for what they can actually do.

That can't stop at the job offer. If we mean it about skills, it has to show up in how we evaluate them, one review at a time.

BY THE NUMBERS

Career changers don’t lack courage—they lack clarity

💡 49% of people say the hardest part of changing careers is figuring out what else they could do. Only 12% cite financial constraints as their biggest obstacle, while nearly 60% want to leave their current industry entirely.

_{Source: State of Career Change Report 2026}

KNOWLEDGE

What to read, watch, and listen

📚 Read: How AI May Reshape Career Pathways to Better Jobs by Brookings Institution

Most conversations about AI focus on jobs disappearing. This article focuses on something more interesting: how AI could create entirely new pathways into better jobs. Brookings explores where mobility opportunities may emerge and which workers stand to benefit most as occupations evolve.

📺 Watch: The Language of Luck by Think Fast, Talk Smart

Stanford's Tina Seelig makes a compelling case that luck is less about chance and more about how we respond to opportunities. Her conversation with host Matt Abrahams explores curiosity, communication, and the habits that help people recognize possibilities others miss. A thoughtful watch for anyone building a career, a business, or a new idea.

🎧 Listen: Workforce Pell, Professional Degrees and a Complicated Spring in Washington from The Key with Inside Higher Ed

Workforce Pell continues to reshape conversations across higher education and workforce development. This episode unpacks the policy debates, funding questions, and institutional challenges that are influencing the future of short-term credentials and learner mobility.

FOR FUN’SIES

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