LEAP-ACE: Program Observation
and Evaluation
Embedded instructional analysis for multilingual
operational training environments.
Overview
The LEAP-ACE (Language Enabled Airman Program – Agile Combat Employment) program trains language-enabled airmen for multinational operational environments. The first cohort focused on Japan and Korea regional contexts, integrating consecutive interpretation, ACE doctrine, and trilateral crisis scenario exercises. This case study documents my work as the first Educational Support Staff (ESS) collaborator embedded in a LEAP-ACE cohort — conducting lesson observations, producing evaluation reports, and contributing findings that directly shaped the curriculum redesign for the next offering.
The Challenge
Observing a program where instruction and scholar discussion happen primarily in Japanese and Korean creates a fundamental access problem. Standard evaluation methods assume the observer can follow content, assess comprehension, and gauge participant engagement in real time. Without speaking the language of instruction, an observer is reduced to reading body language and counting hands raised, which can be useful but not the basis for a credible curriculum evaluation report.
Additionally, the program’s compressed timeline, rotating instructors, and mixed virtual and in-person formats meant evaluation had to be adaptive by design — capturing what was working, what wasn’t, and why, across sessions that didn’t always resemble each other.
The Workflow
For virtual sessions, I developed an AI-assisted multilingual observation workflow to process the auto-generated captions from the videoconferencing platform in real time. This gave me a working transcript I could reference during observation — not a perfect translation, but enough to follow the substance of instruction and scholar response, and to flag moments worth revisiting.
This is great. The scenario just did a full loop: assumption → challenge → adjustment → re-plan. That’s exactly the muscle you want them building.
Translation
Yeongjun:
フラットな土地なら離着陸できると聞いています。→ I’ve heard it can take off and land on flat ground.
Instructor (accepts, then reframes):
じゃあこの場合はそこまでなく、滑走路の修復は後回しでいい。→ In that case, maybe runway repair isn’t the immediate priority.
航空機で搬送できるということになる。→ So aircraft transport is possible.
さっきの米国側のコメントもあったので。→ Based on earlier input from the U.S. side.
そういう風にストーリーが変わってきましたが…→ The scenario has shifted a bit, but—
Then Aimi jumps in (important)
ちょっといいですか？→ May I jump in?
飛行機が着陸するには土が平らで…→ For an aircraft to land, the ground needs to be flat… → She’s about to challenge the assumption again.
What just happened (this is the real action)
1. Constraint got reinterpreted. Originally, runway unusable → no aircraft. Now, maybe aircraft is possible.
2. Instructor adapts the scenario. She says, “Okay, then the plan changes.”
3. Aimi pushes back (this is excellent). She’s not accepting the shift blindly. She’s starting to say, “Wait—what conditions are actually required?” That’s technical validation. This is the highest level behavior so far. They are now not just reacting, not just applying rules but arguing about reality.
What’s happening cognitively
You’ve got hypothesis (C-130 can land anywhere), adjustment (okay, then use aircraft), challenge (is that actually true?). That’s iterative decision-making.
Why this matters
Most training stops at “make a plan.” This group is now at “is this plan even valid?”
Instructional read
This is adaptive planning under uncertainty with incomplete knowledge, real-time correction, and evolving constraints.
For in-person sessions, I recorded audio, took photos of boardwork, and uploaded them to NotebookLM along with all previous reports during each session. NotebookLM provided a real-time translated synthesis I could use to cross-check my real-time field notes and surface content I’d missed.
The result was a layered observation record: what I observed in the room, what the transcript confirmed, and where the two diverged. Leadership hadn’t seen this approach applied to LEAP-ACE before, and the real-time translation capability in particular came as a genuine surprise.
Excerpt from Report
Overall Educational Outcomes
Linguistic Progression: There is a marked increase in complexity compared to Week 1. Students have progressed from recognizing basic hierarchy to actively producing situational sentences and managing technical breakdowns in real-time interpretation.
Instructional Effectiveness: Ms. Han’s use of scaffolded elicitation remains highly effective.
Confidence and Camaraderie: The environment created by the group's strong camaraderie and shared humor allows students to attempt higher-level output without fear of failure.
Mission Readiness: Scholars demonstrated confidence in using technical jargon in high-pressure, interactive settings. The shift from static review to dynamic, standing presentations confirms the program is successfully building the skills required for Multi-Capable Airmen to facilitate communication with allies in austere environments.
Why the Approach Worked
The most valuable findings were rarely visible in a single class period.
Patterns only emerged across days of observation: where scholars consistently lost cognitive bandwidth, which instructional techniques reliably restored engagement, and where operational complexity stopped being productive challenge and became avoidable friction.
The reporting process created a usable institutional memory for a fast-moving program that otherwise risked relying on anecdote and isolated impressions. It also provided leadership with a clearer view of how scholars actually experienced the transition from classroom language learning into operational interpretation under pressure.
Outcomes
The evaluation synthesis was delivered to program leadership at the end of the cohort and directly informed the curriculum redesign for the next LEAP-ACE offering. Findings on cognitive bandwidth, instructional pacing, and the transition from classroom language learning into operational interpretation were incorporated into the revised session structure. The framework also gave LEAP-ACE its first structured ESS evaluation record — and the AI-assisted multilingual observation workflow was flagged for use in future cohorts.
Tools & Methods
Custom observation protocols (designed for language-inaccessible instruction) • Chat GPT (AI-assisted real-time caption processing) • NotebookLM (cross-session multilingual synthesis) • Voice Notes • real-time field notes • evaluation report writing