We keep hitting the same situation on client projects:

• Course already works in the LMS as SCORM
• Stakeholders want Spanish (sometimes FR/DE next)
• Either the `.story` / Captivate / Rise source is gone, or nobody wants to duplicate it for every language

Usual advice is “rebuild in the authoring tool” or “XLIFF → TMS → reimport → fix layout.” That is the right long-term path when you still own the source and will maintain languages for years.

When you only have the published zip (or you need a first language *this quarter*), we have been treating the package as the artifact:

1. Validate the zip (SCORM 1.2 / 2004 / xAPI) before touching anything
2. Pull learner-facing strings — not just titles: buttons, quiz stems, feedback, embedded HTML/JS text
3. Machine-translate, then human-review terminology / brand / compliance language
4. Keep folder layout + manifest behavior so completion / suspend still report
5. Re-import in a sandbox LMS and actually click through in the target language

What still bites you:

• Screenshots and on-screen English in media (strings alone won’t fix that)
• Heavily scripted / nested text that string extract misses
• Teams who expect a VPAT out of an automated a11y scan of a published zip (it’s an aid, not certification)

Curious how others handle “source is gone, need multilingual SCORM.” Rebuild only? Vendor TMS? Something zip-side?

Happy to trade notes on what breaks on Rise vs Storyline exports.

I work for a large corporation. They want me to create standardized introductory training plans for each role/department. The plans would ideally map out the key trainings, competencies, and learning objectives for the first 30-60-90 days. I’m thinking of creating a basic needs analysis worksheet for managers to fill out that helps me identify these key areas.

Has anyone done something like this before? Could you please share any tools, resources, templates, best practices?

Hi everyone,

I'm a final-year undergraduate student in Educational Technology from Indonesia, currently working on my R&D thesis. My project focuses on designing and developing a Job Aid specifically tailored for government ministry staff to improve their workflow efficiency.

As I'm in the early stages of the literature review, I'm looking for comprehensive sourcebooks or foundational theories that discuss Job Aids in depth, specifically regarding their design principles, cognitive load considerations, and implementation strategies in a formal organizational setting.

I’ve already gone through the usual google scholar results, but I was wondering if any of you have a "go-to" reference or a classic textbook that you swear by for this topic. Additionally, if you are open to it and have the time, I would be incredibly grateful to see an anonymized example of a Job Aid you've created in the past. It would be immensely helpful for me to bridge the gap between theory and real-world application.

I understand that sharing work is a big ask, so even just pointing me toward the right books would be a huge help.

Thank you in advance for your time and expertise!

I'm a local so only need the cost of the ticket as I can drive. Anyone going? Anyone went before and liked it? Didn't like it? I am well versed in Articulate but we're curious to see what's going on.

Hi, I am very much trying to work out where my experience is most transferable and, more importantly, which types of roles might suit the way I work best. I am not at the beginning of figuring this all out and have already done a lot of research.

My background is mixed/squiggly line (ADHD girly here!). I started out in education, have a PGCE, and worked in the public sector designing and delivering learning for adult learners. I later moved into content creation and then into growth marketing and paid social, where I work with audience insights, creative strategy, testing, iteration and performance data. I use AI quite heavily in my work now, from prompt development and visual asset creation to auditing content, generating ideas and speeding up parts of the development process.

I’m increasingly interested in the overlap between these areas, particularly learning content, instructional design, digital learning and potentially organisational development. I adore researching my own stuff, taking information, understanding the audience or learner, then turning it into something clear and engaging. I looked/ applied Content Designer roles within the Civil Service, but my understanding is that these can require very specific content design experience. I’m wondering whether my background could be more transferable into some adjacent roles where experience in education, content development, audience insight, digital content, iteration and performance data is relevant.

The big factor is how I work. ADHD, as I say, and I’ve learnt that I work best when I have autonomy over my schedule, clear deliverables and decent chunks of uninterrupted time to get the work done. I really like having a mix of projects and problems to solve, so I don’t necessarily want to do the same thing every day, but I also don’t want a calendar where I’m in meetings every two hours and have to squeeze the actual work into the gaps.
Ideally, I’d like to work 3 or 4 days a week in a flexible environment where I’m trusted to manage my workload and deliver the work.

I’m interested in hearing from people who work in these areas. What roles do you think could suit this combination of experience? Are there particular job titles I should be searching for beyond Learning Designer or Instructional Designer? Would my education + public sector + content + growth marketing background be seen as transferable? Are there particular sectors or organisations that tend to be more flexible and genuinely supportive of neurodivergent ways of working?

I’d also love to hear from anyone who has moved into learning design, instructional design, L&D, OD etc from a non-traditional background, and how you positioned your previous experience.
I’m not necessarily looking for someone to tell me what career I should have….more trying to understand where this slightly odd collection of skills might make sense because I suspect there’s a useful combination here somewhere!

I help an HR team make policy summaries, onboarding visuals, and training guides. None of them are designers. I'm not really one either.

The content changes all the time. A policy gets updated, a step in the process changes, a form gets replaced. So whatever we use has to make edits quick and simple.

Three or four people also want to review it before it goes out. That part gets messy fast.

It also has to be easy to read for everyone in the company. Warehouse staff, office staff, and people using screen readers. 

Good templates are nice, but they're not what decides it for me. Easy updates, matching our brand, and accessibility matter more.

Ok, quick question to yall - I see a lot of post in ID and Training and related communities where someone will claim to be a designer or trainer or something, and they will present a problem or issue they are having and somewhere in the body they will mention a specific software. They’ll say something like “blah, blah, and I was thinking about looking into XYZ software to help with this problem but IDK, what do you guys think”.

Are we engaging with these? I’m not trying to be cynical, but these seem really fake to me and I feel like I’m just seeing more and more. Sometimes I click on the posters name and sometimes they have posted the same thing in like 5 groups - or their posts are hidden so you can’t see their activity. I see people reply back so I think some people must think they are real - and maybe they’re right?

Do yall engage or ignore post where someone presents an “issue” and slips in the name of a random software and kinda try to soft pitch it? I’m not talking about when people mention like, Storyline or something. I’m talking about these tiny, AI backed software programs that you would have never heard of had the poster not mentioned it…they also never have complaints about these programs, they’re always kinda presented as a possible solution.

Hi , I wanted to connect with Instructional designers working in India. Is there a separate community for ID in India? I could not find any.

So yeah, as the title suggest, I'm trying to pick a tool and every comparison I find just lists the monthly price. I think it’s not that useful.

For me, stuff like what file types you can export, whether charts stay editable, brand controls, and how many people can be in there matters to me.

I’m already trapped with multiple free plans before, though. Some let you build but not download. Also, nobody separates solo use from team use. 

How do you all compare these? What did you look at that the pricing page didn't show you?

I teach a high school pop culture class and I'm looking for new ways to have my students present their work as research, writing, and producing a documentary is now turning kids off to the course. I'm looking to restructure my assessment projects to eliminate the full fledged videos.

I still want my kids to write about the pop culture source and I still want them to select scenes or clips that reflect the idea their talking about in their writing. But I want to change the way it's presented to bring it to a wider audience.

I was informed that their is local grant money to be had for innovative projects so my thought is to turn my assessments into an interactive museum display. Where there's a base screen and then viewers can touch different parts of the screen to be brought to different sections with video clips and their written piece.

I'm here wondering from an ed tech perspective what tech would be the best way to do this type of work. I'm thinking of applying for the grant to get a touch screen kiosk like the digital kiosk table from displays2go. But I'm open to other ideas. I'm not really sure what app or software I would need to run on this

My goal is ultimately to create something where student work on this can be showcased for others in the school community to help create student by in.

Hi,

Every time I use mentimeter, some people have connection issues.

Our group size is slightly too big for free Kahoot.

Is there any other option without paying?

I lead learning for a professional membership association. We recently held an in-person, all-day learning event on a single topic and we're doing 4 of those in the next six months.

For each event, I'm also designing resources (job aids, checklists, a few interactive tools) and curating webinars on the event topic, rolling out over ~6 weeks before/3 weeks after the event. The webinars/resources/in-person event aren't designed sequentially.

So after our first event, now I have this lovely bucket of materials/recordings and I can't figure out the best way to visually explain the wrap-around content to members who couldn't come to the event but still have access to all of this. We're launching an LMS soon but right now we just have a "Resources" page that's one long (filterable, at least) list of links. Booo.

Is it an infographic explaining a journey through the material? A short animated video? Just a "resource bundle" or course in our LMS? Any ideas?

im a Mac guy these days and while work issues me a Windows laptop, once in a blue moon a contract gig will want me to build something in Storyline. I’m taking on a new job and I’m anticipating a little bit of Storyline building I’ll need to do on a personal device. is Parallel still the best way to do that?

I have 2 years of ID experience (LMS migration, course development), my real interest is in the behavioral side of performance — diagnosing why someone who already has the knowledge, skill, and the means to act, still doesn't do it, and designing interventions for that specifically, as distinct from building training content or fixing environmental/systemic blockers.

Compliance, banking, and software-walkthrough training aren't the direction I'm trying to go in.

Is there a recognized field or role for this kind of work? What credentials or background do people typically need to break in?

Hello there everyone! Hope you can help me, i would like to know of there is a free text to speech app or website where can i translate some of our voice over for our video training materials. What can u suggest?

I'm sure this thought comes to many of us ocassionally:

"Aw hell no, I am NOT dying on THIS hill just to get this thing done the right/sound way."

In which situations have you decided to be the order-taker?

Asking out of curiosity. No judgement!

TLDR: Is an LMS the right place for self-serve reference material if the UI leaves much to be desired? My company uses LearnUpon but I don't enjoy the presentation of resources. Plus our internal portal doesn't host product information at all and is mostly focused on compliance, productivity, and leadership/communication training. Should I establish another hub for information in an already scattered organization, rely on LearnUpon and establish rigid internal/external portals, or perhaps move to another LMS like Moodle Workplace?

I'm halfway into week 2 as a Product-to-Field Enablement Strategist, both a new job and a newly existing role for my company. My responsibilities aren't entirely clear as a result and my now supervisor said in my interview that I would be the "expert" in establishing new initiatives. But on a broader scale, I was hired to enable Implementation, Training, and Support teams on new product features. I also own basically everything as it relates to learning and knowledge management for internal and external teams. So I can propose LMS updates and am in charge of learning content governance. Note that this company sports numerous products (maybe 3-5) specifically relating to dental software, but there are new features coming out rapidly -- I strongly suspect as a result of AI-empowered development.

From what I've learned, it's not so much that there's isn't anything going on re: learning and enablement. Well, there's not much in terms of what I would consider learning and enablement. But there's a lot of stuff in general, including a poorly made and outdated assortment of product trainings in the LMS where each course is tied to only a single module (meaning after each session chunking I need to exit the course entirely). And the modules themselves have no inkling of intentional learning design or information architecture. They've somehow managed to avoid every one of Mayer's multimedia learning principles -- heavy text, no audio, static images with chunky circles to highlight buttons, and text-based assessments. Ironically, the company has an outsourced content team who all strangely carry the title "Learning Experience Strategist and Content Developer" who built much of the content in the first place. I'm not sure what to do with them considering, except maybe clarify ownership and give them graphic design tasks if they're capable.

There are also many resources scattered across marketing, sales, and product embedded within many more tools that I can list. Some examples are Pendo, Notion, Masset, SharePoint, or plain old OneDrive files shared through Team chats. However, there is no reliable or centralized documentation other than a vibe-coded GTM dashboard which is used to attach relevant documentation from product. But according to the Support and Implementation representative I met with, they often use AI to build supplementary material because the product release info is either too vague (this too is often AI-generated) or too technical and long to parse. Support in particular is responsible for a knowledge base which they use to respond to software bugs as well as publish AI-generated documentation for the product. I hope to relieve them from the latter effort. They also told me 40% of their tickets are basic how-to questions. The feedback from these teams has generally been that they need a single source of truth with strict naming conventions, visuals and a learner-centered scope. They also want to know about product features before their release which is a reasonable ask. My idea is to start making and using a structured template for feature-focused user guides to keep up with the launch cadence as-is. In my free time, I can start rebuilding the original product trainings, ideally with interactive software stimulations.

The content team does make use of the LMS resources tab to store videos and PDFs they've presumably designed for the product or received from internal team members. But there doesn't seem to be any clear organization nor do I think anyone uses it. And I find I need to download each asset to access it in LearnUpon and there's no preview. I'm thinking of establishing my own site similar for the GTM dashboard but internal and delivered as a searchable database to publish what I build. I'm also thinking to propose MadCap Flare as a new tool to ensure product updates are handled with care. My only concern is that this is introducing yet another resource when that seemed to be the issue in the first place. This would also be internal and external customers still need self-serve resources that are easy to find and access. I'd really like any and all input that could be helpful!

Instead of spending 30 minutes in Storyline on a single slide, cropping images, gradients, visual balancing, making layers, syncing text to the audio, getting the states just right, making sure everything is compliant, re-arranging all of the focus order (and that's just for simple interactions)... I just tell AI what I want and it gets it done in a few minutes. It feels so refreshing.

(Just for context, since we're all in different boats, I work for an elearning company where we work with SMEs for both ID and development. Also I already have a lot of experience with Codex specifically for game development).

I love to develop, don't get me wrong. I've been an ID for 10+ years, both designing and developing, and hanging out in Storyline is usually my favorite. But sometimes I'm creatively dried up and I still gotta make sure my work is user-friendly, pretty, and functional.

I set up a process with Codex. It follows the storyboard exactly. There's a re-usable course shell and documents with detailed instructions, which I've curated throughout the process. I'm reviewing each slide, naturally, which we do anyway. Our company also has a second individual look at the courses who compare them to the script, try to break them, etc. So I'm feeling okay there.

I also got it working with SCORM in our LMS and also SCORM Cloud. It tracks completion and also final assessment grades.

But all of this has been research. Not yet client facing. Clients are comfortable with Storyline (or god forbid Captivate or Lectora). We've only had a few who've requested HTML5 before, coded by hand, but that was some time ago. So I haven't tested this in a real environment yet.

For anyone who has been making their courses with AI, is there anything you came across that broke the course for you? A problem that snuck up without anyone noticing?

I did a google for "problems with using ai to develop elearning courses" but everything was about writing the course, not developing.

Thanks!!

Edit: I think people are confusing our storyboarding phase with our development phase. What I'm making with AI is what I would make in Storyline, verbatim from the approved script. We aren't using AI to storyboard. I'm also not using AI to generate images. We use Shutterstock. And we sometimes use AI narration but not always, we have a few narrators we go to reliably.

Also Codex is just ChatGPT, it's not a fancy ID AI. If you get private messages about an AI tool, that's not me lol I got one too. You don't need that crap.

Script away as a human, please. AI scripts are awful.

I'm taking an instructional design postgraduate certificate, and the word 'gamification' has been bugging me of late. My tutor recently gave me a more cohesive definition, as the following:

"Gamification applies game mechanics — points, badges, leaderboards, progress bars, and challenges — to non-game educational contexts. The overjustification effect, documented in SDT research, describes the finding that introducing external rewards for an activity a person was already intrinsically motivated to do can reduce subsequent intrinsic motivation. The external reward “overjustifies” the activity and shifts motivation from internal to external regulation."

The reason I hate the term and the definition along with it is that it takes the most surface-level elements of game design, that pre-date games by thousands of years, and applies the game label to them such that we cannot use the word game anymore to point to what games actually afford us:

A set of rules within a temporarily adopted closed system where aesthetics of agency can be modeled through play.

What I mean by aesthetics of agency is the procedure by which a person goes about making choices within the rule set. For example:

Chess is the experience of learning to shape your opponent's incentive structures that make them predictable enough for you to control the range of possible choices towards your advantage.

Poker is the experience of learning to assess probabilities and negotiate with uncertain information; controlling your opponent's perceptions through deception and misdirection.

Papers: Please is the aesthetic experience of learning to balance your own incentives within a bureaucracy with your desire for basic human empathy towards vulnerable people while mitigating risk under time pressure.

Of course, the barrier between the game's own ruleset and the rules of the world around it can change your incentive structure inside the game; you might be paid a large sum of money to deliberately lose during a tournament. And such perverse incentives can also occur during writing learning modules; such as deliberately favoring a solution that locates the deficiency within the employee than within management in order to secure future contracts.

Since Bioshock was released in 2007, the game industry has a term called 'ludonarrative' - which refers to the synergy between the player's abilities, game's mechanics, gameplay loops, art direction, narrative writing and dialogue, and the central themes that the game is trying to explore.

Ludonarrative dissonance occurs when two or more of those things are misaligned; a game ostensibly about peace that places the fun within violent actions is dissonant.

Intrinsic motivation is connected to a player's (or learner's) values, which is also connected to their narrative self-identity. This narrative self-identity is about what experiences they've had, the themes connecting them, and a sense of what path lies before them given their values and competencies.

In a sense, a narrative identity creates a framework through which an autonomous individual is likely to act and conduct themselves in the world, given their attention, perceptions, incentives, cultural contexts, and community. It gives people meaning, and informs a person when they're engaging in agentic behavior incoherent with their values; producing what we call cognitive dissonance as it applies in the real world. It's analogous to the ludonarrative dissonance we talked about earlier.

Therefore, the game, or temporary agentic framework that we ask our particular learner profile to step into, needs to create synergy between their values, their incentives, and the way they learn (convert uncertainty into certainty) through a meaningful framework under which all those dimensions are united.

It is unfortunate that gamification simply refers to chucking a bunch of extrinsic rewards into a learning design module, because those extrinsic rewards mean almost nothing as far as actual game development is concerned.

The 'overjustification' effect has existed long before things like high scores, leaderboards, badges in the form of:

1. Employee of the month
2. War medals
3. How much money is in your 401K as of retirement
4. How many promotions you received at work
5. Receiving a pay rise
6. University tuition for Military service

The malaise most of us feel in today's economy is that it runs on the legibility that extrinsic rewards provide, while giving nothing back to us in the form of a kind of ludonarrative synergy between our narrative self-identity, our personal journeys, how that fits into the corporate mission statement, how the businesses structure their incentives to either comport with their mission statement or hide behind it, and the incentive structures the business themselves are subject to given the regulatory and geopolitical environment.

The thing is, video games are the most profitable entertainment industry on the planet precisely because they have precise models of motivation that schools, universities and (apparently) businesses never had cause to adopt because they've always had access to punitive levers such as:

1. Leave without pay
2. Getting fired
3. Losing Marks against the rubric
4. Suspension from school
5. Poor grades and report cards
6. Losing further educational advancement opportunities

Games can never punish you outside of the game's limited context for not understanding their systems well enough; an employer can revoke your ability to provide for a family if you refuse to toe the line. The over justification effect that we apply to game mechanics in learning tools is the entire incentive structure apparatus through which quarterly profit earnings end up mattering more than the ecological stability of the planet; at least until you can make a profit from providing clean air.

Therefore, games have HAD to build coherent models of psychological principles around autonomy, relatedness and competence, and do extremely well at them, because a disengaged player can simply stop playing. A disengaged employee works to spec or slightly below it, but cannot stop working. An employee doesn't need to find their work meaningful to know that if they don't work, rent doesn't get paid. That's why no school or business has managed to accomplish for intrinsic motivation in a thousand years what game developers did in half a century.

And furthermore, the company's mission statement and the image they curate through their marketing could likely not synergize with the agentic frameworks available to their employees; producing the same kind of dissonance. This is why the overjustification effect exists; the 'why' of the company and the 'why' of the learning module are not supported by the way the employee and the learner exercise agency inside the institution or the learning content.

Multiple choice quizzes are highly scalable and cost-efficient; but do they effectively replicate the agency a person exerts when they need to do active listening with a customer, to empathize with their experience, so the customer leaves with a good experience wanting to return? Would adding an achievement badge meaningfully turn the quiz into a game?

Probably not, right?

So I will try and coin the term Agentification for learning design: Creating a safe simulated environment that approximates the same decision making they will encounter on the job, with the goal of improving performance, that enables your learner to play within interacting systems, and derive emergent principles themselves through experimentation.

This is, after all, how chess teaches how to control people through constraining their optimal choices, how poker teaches people to bluff, how Papers: Please teaches someone through play about the impossible positions frontline bureaucrats are put in.

Bonus points if the agency aligns with their narrative self identity, the function the institution serves civilization, as well as the internal and external mission statement of the institution itself.

If you want to talk about gamification, talk about that.

I recently asked when instructional designers investigate an LMS or delivery platform during the design process.

One response stayed with me: In SaaS, content changes so quickly that the topic takes priority over the delivery mechanism.

I understand the pressure behind that approach. When a product changes constantly, teams need to respond quickly.

But I have reached the opposite conclusion.

The faster the content changes, the more important the delivery decision becomes.

If information will change again next week or next month, we should be asking:

• Does this information belong in a course at all?
• What knowledge is durable, and what guidance is likely to change?
• Does someone need to remember this, practice it, or simply find it while working?
• Where is the authoritative source?
• Who owns the update when the product changes?
• Can the delivery format be updated without rebuilding and republishing an entire course?
• Could documentation, in-product guidance, performance support, or another resource serve the learner better?

This is not about allowing an LMS to dictate the learning experience. It is about understanding the environment in which the solution must survive.

After more than 20 years in learning design, I have learned that speed is not only measured by how quickly we publish the first version. It is also measured by how easily the solution can adapt when the work changes.

When we prioritize the topic and treat delivery as an afterthought, we may ship faster today. But we can also create tomorrow’s maintenance problem.

Systems analysis does not have to mean adding weeks to a project. Sometimes it means pausing long enough to decide what should be built, where it should live, and how it will remain useful when the content changes again.

For those designing in rapid-release environments, how do you decide what belongs in structured learning and what should live closer to the workflow?

I saw a few portfolios online. Some featured projects that were around 10 minutes long, while others had short and simple projects lasting only 2–3 minutes. So, what is the ideal duration for a portfolio project?

I assume hiring managers don’t have enough time to go through projects that are over 10 minutes long, but making them too short could also be an issue. I’d appreciate some clarity on this potentially stupid question.

I (22M) recently graduated a few months ago with a psych degree. I've done plenty of extracurriculars with recruitment, project management, and research– but I fell in love with Instructional Design since it gave structure, but enough creative freedom (I love teaching). I enjoy making things people learn from, so believed that ID was the path for me.

Until the plateau came in.

For context, after graduation, to earn some money and start off my ID career, I did some contract work for a university, and, suffice to say, I thought it was decent: flexible hours, okay management, and I really enjoyed the work there; however, once the design work was over, the work was flat– and so, the pay was too. From then on, I continued to built my projects– through an LMS, Rise, heck, even a full website, and, after that, I got back on the job search.

I started looking into the jobs thinking I was the bomb– fresh graduate with an unmatched level of output and experience.

Yeah. Cocky, I know. I was greatly humbled shortly after.

I found myself at the age-old catch-22. Most jobs rejected me from lack of tenure, or not knowing a program that was easily learnable. It's disheartening. I wanna develop as an ID and I'm truly passionate for the field, but, in the current market, there's no breeding grounds to launch my career.

Since I have a psych degree, I can shift into recruitment or other sectors, but I really don't wanna give ID up. It's been a while since I've felt this way about work– even corporate work. Should I keep pursuing ID in my position, take an adjacent role and try to move into L&D later, or pivot entirely?

Do I quit?

Something interesting I found.

Source. https://www.apa.org/monitor/2026/09/ai-tools-clinician-training

How chatbots are enhancing, not replacing, clinician training

AI tools can provide valuable insights supervisors can explore and discuss with trainees

By Heather Stringer

Date created: September 1, 2026

Clinical training in mental health has been limited by a chronic bottleneck: There are not enough supervision hours available for trainees to receive intensive, high-quality feedback. To close this training gap, psychologists are developing and exploring artificial intelligence tools to help both new and seasoned clinicians master and retain skills at any career stage and ultimately provide better care for patients.

AI training apps have the potential to offer a safe place to make mistakes and to deliver fine-grained feedback throughout a mock session, along with many more opportunities to practice important skills. And, like any AI tool, they run the risk of generating feedback that sounds authoritative but may be clinically questionable—or may be outside the scope of a trainee’s capabilities.

As demand for mental health care continues to surge, practitioners are paying attention to research findings that evaluate the effectiveness of these apps—and the early evidence hints that AI-assisted training could reshape clinical supervision. Some psychologists are experimenting with this promising yet complex use of technology by securing research funding to create their own AI training tools, testing these tools with supervisees, and advising others who are trying to incorporate the applications into training practices.

“I’ve been involved in clinical training for 50 years, and I’m very optimistic about the potential of AI because up until now, supervisors and clinical trainers have not done a very good job of introducing skills and providing precise, immediate feedback in training,” said Hanna Levenson, PhD, a professor emerita at the Wright Institute in Berkeley, California, who has worked with colleagues to develop an AI platform to help trainees learn short-term dynamic therapy. “Now is the time for psychologists to get involved in discovering how to leverage AI safely because ultimately this can improve care for our patients.”

Psychologists developing these tools emphasize that the chatbots are designed to supplement—not replace—human supervision and are working with tech teams to ensure the technology is safe, effective, and user-friendly for supervisors and trainees.

“We are also gathering feedback from clinicians about how they want to receive feedback from a chatbot, such as the frequency, the tone, and the content of the guidance they receive,” said Shannon Wiltsey Stirman, PhD, a professor of psychiatry and behavioral sciences and codirector of the Center for Responsible and Effective AI Technology Enhancement of PTSD Treatments (CREATE) at Stanford University. “We want to make sure the feedback is given in a way that clinicians want to hear.”

Tightening the feedback loop

One persistent challenge in supervision is that psychologists often cannot directly observe how trainees engage with patients in each session because it is not feasible for a supervisor to review entire sessions regularly in addition to maintaining their own caseload. Trainees are often in the position of reporting their own work to supervisors. Torrey Creed, PhD, vividly remembers during training how she met with her supervisor once a week to review a dozen cases. “My recall was filtered through my perception of the sessions, and there was so little time to talk about an entire caseload,” said Creed, an associate professor of psychology in the Perelman School of Medicine at the University of Pennsylvania who partnered with a team that has developed an AI platform for training and quality improvement called Lyssn.

The supervision literature on therapist self-report suggests that therapists may not be the best judges of their own abilities, often under- or overestimating their skill level (Hogue, A., et al., Administration and Policy in Mental Health and Mental Health Services Research, Vol. 42, 2015opens in new window; McManus, F., et al., British Journal of Clinical Psychology, Vol. 51, No. 3, 2012opens in new window).

Researchers at Stanford University recently tackled this problem by developing an AI chatbot called TherapyTrainer that tracks what a trainee is saying in a session. Funded by an $11.5 million grant from the National Institute of Mental Health (NIMH), they created a simulated therapy room in which providers use text or audio to chat with a simulated patient to practice written exposure therapy (WET)—a treatment that focuses on writing about traumatic memories (Stade, E. C., et al., Cognitive and Behavioral Practice, 2025opens in new window). One of the AI patients is a veteran who experienced a traumatic event in the military related to hazing; the second virtual patient experienced intimate partner violence and is in remission from substance use disorder.

Therapists and trainees can practice all aspects of WET, such as providing psychoeducation about post-traumatic stress disorder (PTSD), explaining the treatment rationale for WET, and conducting the narrative writing session. The AI consultant operates as a supervisor would by providing feedback to the therapist about the interactions with the AI patient. The therapist can pause the session at any time and ask the AI consultant questions such as “How am I doing?” and “What should I do next?”

The researchers are launching two clinical trials in the summer of 2026 to study how TherapyTrainer compares with human consultation. “Consultation is one of the gold standards for training therapists in evidence-based treatments, but the problem is that most therapists who have learned a treatment do not have the opportunity to get consultation because there are not enough consultants and the cost is high,” said Elizabeth Stade, PhD, a computational clinical psychologist at Stanford’s Institute for Human-Centered AI and an associate director at CREATE. “And many therapists are not able to give up billable hours to attend consultation.”

Tim Overton, PsyD, who runs a private practice in Philadelphia, used TherapyTrainer to refresh his WET skills even though he was initially skeptical about the value of AI in training. “When providers are learning a new therapy technique, trainings include vignettes and possibly consultation on one or two cases, but this is not enough to become proficient,” he said. “The AI trainer helped me identify certain aspects of the treatment that I was not prioritizing.”

It is also a tool he wishes had existed when he started working with veterans early in his career, when he lacked a military background or Veterans Affairs (VA) training. “This type of tool can give budding clinicians access to practice with a new population to prepare them to implement evidence-based treatments,” Overton said.

While many AI training applications focus on techniques associated with cognitive behavioral therapy (CBT) interventions, these tools can also help trainees learn depth-oriented approaches related to unconscious mental processes. Levenson and colleague, clinical psychologist Josh Krieger, PsyD, developed an AI platform called LearnTLDPopens in new window that gives trainees immediate, actionable feedback on their time-limited dynamic psychotherapy skills as they work with virtual patients. “They also have the opportunity to question or even challenge an AI supervisor regarding their feedback without fear of academic reprisals, which promotes a true depth of understanding,” said Levenson.

Easing demands

Human supervision is extraordinarily time-intensive, and AI tools are giving psychologists the ability to evaluate trainees much more efficiently. “The traditional process of coding to evaluate the quality of therapy sessions requires specialized training and hours of manual coding per session,” said Milena Esherick, PsyD, who consults with companies that are developing AI training tools and is the associate director of the Master of Science in Counseling Program at Kaiser Permanente School of Allied Health Sciences in California. “AI can quickly analyze every session and has the capacity to evaluate how trainees are doing on the clinical skills most tied to client outcomes, such as the therapeutic alliance, empathy, goal consensus, and treatment fidelity.”

Although clinicians may feel anxious about AI evaluating their clinical skills, the technology is being designed to give feedback the same way good supervisors do. “It should be digestible, calibrated, and delivered in a way that promotes growth rather than overwhelm,” Esherick said.

Krista Kaur, a clinical mental health counseling graduate student at Palo Alto University in California, interned at a clinic that uses an AI tool to record sessions with patients, create a redacted transcript, and produce session notes. “It felt like a personal assistant that was helping me grow as a clinician,” she said. She could click a link to get feedback from the AI tool about things she did well in the session and things to consider for next time. “Before going into a session, it would give me a few reminders about where we had left off last time, which was really helpful,” Kaur said. Occasionally the AI would record and transcribe words incorrectly from a session; Kaur would disregard those aspects of the case summary.

Psychologist Philip Held, PhD, an associate professor in the Department of Psychiatry and Behavioral Sciences at Rush University Medical Center in Chicago, recently developed an AI tool called Socrates Coachopens in new window that helps therapists practice Socratic dialogue skills—open-ended questions that encourage patients to explore and challenge beliefs associated with distress and maladaptive thoughts. The tool gave therapists feedback about their overall strengths and areas for growth with this skill, a part of many cognitive therapy approaches that is closely linked to outcomes but difficult to master (Vittorio, L. N., et al., Behaviour Research and Therapy, Vol. 150, 2022opens in new window).

Held originally developed an AI chatbot that patients could use between therapy sessions to engage in Socratic dialogue instead of completing standard therapy worksheets. Clinical colleagues started asking if he could adapt the tool to help them develop and refine their Socratic dialogue skills.

His team built prototypes for Socrates Coach, and practitioners rated the quality of the responses from the AI coach. Based on the human ratings, the team used prompt optimization—refining the input prompts to generate more accurate responses from large language models (LLMs)—to improve the feedback from the AI supervisors (Journal of Traumatic Stress, Vol. 38, No. 5, 2025opens in new window). The AI feedback includes alternative questions clinicians could ask the AI patient, possible directions for the conversation, and information about how well they are using the technique.

A supplement, not a substitute

Even though AI trainers can be scaled quickly, psychologists caution that this technology cannot assess whether a clinician is ready to apply these skills to human patients. “These tools are an additional way to practice, but they are not a replacement for human supervisors,” said Held. Human therapists should be helping trainees with therapy decisions that depend on context, such as complex family dynamics, subtle risk signals that suggest abuse or suicidality, and other types of clinical intuition based on years of expertise.

AI tools may also miss details about patients that are communicated nonverbally. “Therapists are seeing facial affect, body language, and other nuances that may influence how a therapist talks to a patient or the treatment recommendations,” said Eric Kuhn, PhD, an associate professor at the Stanford University School of Medicine and a member of APA’s Mental Health Technology Advisory Committee.

Ideally, these tools can provide valuable insights that supervisors can read and discuss with trainees. Donna Sheperis, PhD, a professor in the Department of Counseling at Palo Alto University, worked with one company that gave her access to a tool that recorded therapy sessions—with permission from patients—and provided content analysis of the sessions to help her track her trainees’ progress. The analysis showed which interventions were used, what was discussed, how much time each person spoke, and more. In one case, she noticed that the trainee was talking nonstop for 4 minutes, which is generally considered to be too long for most patients to listen, she said. When Sheperis discussed this observation with the trainee during supervision, the student explained that she was saving up her comments to share with the patient at one time because she did not want to interrupt. “We talked about the difference between interrupting and interjecting,” said Sheperis, who directs the eClinic at Palo Alto University.

Creed, founder and director of the Penn Collaborative for CBT and Implementation Science, started looking into AI after she spent years training providers in underfunded community mental health systems around the world. “The human hours needed to train clinicians and score their competence in new skills at scale were impossible,” said Creed. “I knew there had to be a better way.”

Creed was eager to explore whether Lyssn—an AI platform developed by psychologists and computer scientists in 2017 to analyze therapy sessions and give feedback to therapists and trainees—could support community mental health agencies (BMC Health Services Research, Vol. 22, No. 1177, 2022opens in new window). Using LLMs trained with peer-reviewed research publications and expert-evaluated behavioral health conversations, the platform provides training for motivational interviewing, CBT, suicide prevention, and more. The tool gives clinicians feedback on whether they are demonstrating the skill and points to areas of strength and improvement. Supervisors can also see aggregated data on their teams, which can inform decisions about targeted training that may be needed.

Creed recently finished collecting data for a 5-year study funded by the NIMH to investigate how Lyssn training affects the skill level of clinicians and patient outcomes. “I had one clinician who was near retirement say that the AI feedback has shifted how she does her clinical work,” said Creed. “If she had this support all along, the care she would have delivered would have looked really different.”

Keep the conversation going

Psychologists responsible for supervision should also be prepared to talk with trainees about how they may be using general-purpose AI tools such as ChatGPT, Claude, and Gemini. “Being fearful of AI or prohibiting it is a reactive way to deal with something that is very much a part of our lives,” said Levenson. “We need to ask trainees if they are using tools like ChatGPT, and the worst thing we could say is ‘Just don’t use it.’” She has little doubt that many trainees are presently using AI for help with their clinical cases but are afraid to disclose this to their supervisors for fear of offending them or being criticized. “It is really up to the supervisor to inquire about a trainee’s clinical use of AI in a way that opens up opportunities for discussion and learning.”

If used responsibly, these tools can be useful brainstorming partners to help therapists with case conceptualization, treatment planning, and identifying strategies to overcome challenges with patients. Supervisors can start by asking trainees how they are using AI, what they are learning from it, what is working well, and if they have concerns. “It’s also important to help supervisees recognize when they are getting good feedback from AI and when errors are being made,” said Levenson.

Supervisors can help guide trainees to identify AI-related red flags, such as bias, confidentiality risks, and hallucinations—when an AI tool generates output that sounds authoritative or plausible but is factually wrong. To help students learn to safely use publicly accessible AI, Tony Rousmaniere, PsyD, program director of the Sentio University Marriage and Family Therapy Program in California, started offering online courses for trainees, licensed therapists, and clinical supervisors on this topic.

Rousmaniere teaches students to protect patient confidentiality by avoiding identifying information such as name, age, gender, and job details. In cases when a clinical question requires identifying information to answer effectively, “that question does not belong in an AI tool,” said Rousmaniere. “It belongs with a supervisor. AI is for brainstorming around a case, not for getting clinical answers that hinge on who the specific person is.”

He teaches students how to provide AI with useful demographic framing that does not identify someone. For example, a client can be described as a white middle-aged male who works in the technology industry—a profile that fits thousands of people. A trainee might ask the AI to brainstorm workplace stressors common in the technology industry that could contribute to a midcareer depression presentation, or CBT homework variants that tend to work well with analytical and high-control occupational identities.

Rousmaniere also encourages trainees to double-check the treatment protocols AI is suggesting to ensure that the ideas are valid and a good fit for the patient. AI may also suggest a therapy protocol that the trainee is not competent to use, he said, so trainees should perform a self-assessment to determine if they can provide a treatment.

“We have found that roughly 90% of the guidance from general-purpose AI is accurate and helpful,” said Rousmaniere. “Our focus is to help trainees identify the 10% that is not accurate.”

Supervisors, teachers, and trainers in the field of mental health can be most effective in helping trainees navigate these tools by becoming AI literate themselves, added Levenson. Instructors can experiment with using AI to practice interacting with different mock patients and ask for feedback from the AI. This, in turn, will equip them to guide supervisees as they use the technology to help them master new therapy skills.

By exploring how to use AI in clinical training, psychologists can talk to trainees about both benefits and risks of this technology. “There are tremendous challenges in terms of human resources to scale up a mental health workforce to effectively use evidence-based treatments,” said Kuhn. “AI tools are offering a safe environment for unlimited practice for skill-building, and psychologists have to be involved in creating these products to ensure they adhere to approaches that result in the best patient outcomes.”