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WORKPLACE
LEARNING
TECHNOLOGIES
ADOPTION
HINT: AI IS EVERYWHERE
(EVEN WHERE IT’S NOT)
Megan Torrance
and Lauren Milstid
© 2025 The Learning Guild. All rights reserved.
The Learning Guild
489 5th Ave, 5th Floor
New York, NY 10017
Written by: Megan Torrance & Lauren Milstid
Director of Research: Jane Bozarth, PhD
Publication Design: Jennifer Hoeke
DISCLAIMER
The ratings, information, and opinions set forth on the Guild Research
section of The Learning Guild website, and in the Guild Research
charts and graphs found in this report, are those of the members of
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Content” section of the Privacy, Membership, and Terms of Use
Agreement athttp://elgd.co/tos.
LICENSE AGREEMENT
See page 34 for full terms of use.
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About the survey & methodology
The purpose of this survey was to explore the adoption timelines
of various learning technologies among workplace Learning and
Development (L&D) professionals, with an emphasis on Learning Guild
members.
This survey also included a separate set of questions for providers of
learning products and services to explore how their experience might
be similar to or different from that of their clients.
This survey limited itself to questions about the arrival date of each
technology within the organization, not the adoption diffusion among
users or other success measures of the technology implementation.
The Learning Guild conducted the survey online during February and
March 2025. The invitation was distributed twice to 36,5000 Learning
Guild members via email, as well as through social media posts from
both the Learning Guild and the report’s author, Megan Torrance.
The intended audience for the survey was workplace L&D professionals.
Based on responses to our question about job title, the overwhelming
majority of respondents aligned with this target audience.
A total of 556 individuals submitted unique responses, composed of 502
workplace L&D professionals and 54 providers of learning products and
services.
survey fatigue
a phenomenon where
individuals become tired,
bored, or frustrated
with participating in
surveys. This can lead
to decreased motivation,
lower response rates, and
unreliable survey data.
To minimize survey fatigue, we broke
up a very large list of technologies
into two sets. 502 L&D respondents
answered questions about a core set of
20 technologies. Of those respondents,
152 opted to answer questions about a
second set of 23 technologies.
• With 502 responses from a mailing
list of 10,000, we can be 95%
condent that the survey results
reect the views of the larger group
within a margin of error of ±4.4%.
This means if 70% of respondents
selected an answer, we can be 95%
condent that the true percentage
in the full population of Guild
members falls between 65.7% and
74.3%.
• For the subset with 152
respondents, the results have a 95%
condence level with a margin of
error of about ±8%.
Core set of technologies
1. SCORM-Based eLearning
2. xAPI for Learning (Tin Can API)
3. Learning Management Systems (LMS)
4. Learning Experience Platforms (LXP)
5. Learning Content Management Systems
(LCMS)
6. Virtual Classroom / Webinar Tools
7. Mobile Learning / Apps
8. Video-Based Learning Platforms
9. Performance Support / Job Aids
10. Gamication Engines
11. Virtual Reality (VR) for Learning
12. Augmented Reality (AR) for Learning
13. AI-Based Learning Personalization
14. Chatbots for Learning
15. Generative AI for Content Development
16. Automated Translation /
Localization Tools
17. Screen Reader / Accessibility
Solutions
18. Community of Practice / Discussion
Board Solutions
19. Digital Badges / Micro-
Credentialing
20. Learning Analytics / Data
Visualization Platforms
Additional set of technologies
1. xAPI for Non-eLearning Activities
2. Mentoring / Coaching Platforms
3. Microlearning Solutions
4. Live Polling / Quizzing Tools
5. Screencasting / Screen Recording Tools
6. Immersive Gaming Simulations
7. Telepresence Solutions
8. Virtual Labs / Software Simulation
Platforms
9. Adaptive Learning Systems
10. AI Chatbots for Personalized Learning
11. Robotic Process Automation (RPA)
12. Intelligent Tutoring Systems
13. Automated Content Curation /
Recommendation Engines
14. Survey / Feedback Platforms
15. On-the-Job Performance Tracking /
Workow Learning Systems
16. Proctoring / Remote Exam Solutions
17. 360-Degree Feedback / Assessment
Systems
18. Learning Record Store (LRS)
19. Skills Mapping / Competency
Management Tools
20. E-Portfolio Tools
21. Voice-Enabled Learning Solutions
22. Biometric Engagement Detection /
Analytics Tools
23. Blended Learning Orchestration Tools
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Survey respondents represent a diverse cross-
section of the workplace learning industry,
spanning internal L&D professionals and
external providers. To better understand
the perspectives of those working inside
organizations versus those supporting them
from the outside, the survey branched based
on respondents’ organizational type. While the
core questions remained similar, this structure
allowed for a more nuanced look at how
different roles experience and inuence the
learning industry.
The top 5 types of organizations
respondents work for are:
• 23% large corporations with more than 5,000
employees.
• 15% medium corporations with 1,001-5,000
employees.
• 13% small corporations with 1-1,000
employees.
• 10% organizations that primarily sell learning
products or services, such as a vendor or
provider.
• 10% self-employed or a freelancer.
Nearly half of survey respondents work
in the for-prot private sector
4%
Other
8%
Higher Ed
9%
Non-prot
10%
Provider / Vendor
23%
Large Corp.
15%
Medium
Corp.
5%
State or
Local Gov.
3%
Federal Gov.
10%
Freelancer
13%
Small Corp.
Data from providers is segmented for separate analysis.
N = 556
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The survey collected both role-based and
geographic demographic data to provide
context for interpreting the results.
Individuals were asked to report on the
practices of their organization rather than from
the perspective of their specic role. As such,
differentiating by role was not necessary.
The top 3 roles in the learning industry
among respondents are:
• 40% instructional designers, developers, or
learning experience designers (LXDs).
• 27% L&D managers, directors, or chief
learning ofcers (CLOs).
• 8% learning consultants or business analysts.
8%
Learning
Consultant or
Business Analyst
7%
LMS
Admin
27%
L&D Manager
or Director
CLO
1%
Sales
40%
Instructional
Designer /
Developer /
LXD
8%
Other
1%
Learning
Engineer
2%
Project
Manager
4%
External
Provider
1%
Learning
Data
Analyst/
Data
Scientist
Most of our survey respondents are in
roles responsible for selecting and using
learning technologies
N = 556
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While there was some international
participation, the geographic breakdown was
not statistically signicant outside of North
America, limiting meaningful comparison by
region.
The geographic representation
among respondents is:
• 75% of respondents’ organizations are based
in North America.
• 10% are in Europe.
• 15% are from other regions.
75%
North America
10%
Europe
1%
Latin
America
8%
Other
5%
Asia-Pacic
2%
Middle East
& Africa
Survey respondents were primarily
from North America
N = 556
KEY FINDINGS
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We explored current adoption and future plans
across 8 timeframes
To understand where different learning technologies stand today, we asked respondents the following
question: “For each technology listed below, please indicate approximately how long your organization
has been using it (if at all) or when you expect to adopt it.”
Respondents were rst shown 20 core learning technologies. To reduce survey fatigue, they were then
given the option to opt in to assess an additional 23 technologies. In total, up to 43 technologies were
evaluated across 8 possible adoption stages:
• Using 10+ years
• Using 5–10 years
• Using 2–5 years
• Using <2 years
• Plan to adopt in next 12 months
• Plan to adopt in 1–3 years
• Not sure
• No plans to adopt
For the purposes of this report, we have grouped all of the “Using …” responses together to indicate the
adoption rate of a technology. In our charts, we sort technologies within each group by the proportion of
respondents who indicate they have been using a technology for 10+ years.
We advise caution against reading too much into the “Not sure” responses. “Not sure” may indicate that
a technology has been adopted (but the respondent is not sure when) or the respondent is not sure if
the technology has been adopted at all in their organization. For the purposes of this report, we have not
focused on the “Not sure” category.
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How to read the charts that follow
The survey results reveal signicant variation in how these technologies are used across organizations. To help interpret
these patterns, we grouped the tools into four adoption groups:
Mainstream: 80% or more of respondents indicated that their organizations have adopted these technologies.
Common: 50-80% of respondents indicated that their organizations have adopted these technologies.
Selective: 20-50% of respondents indicated that their organizations have adopted these technologies.
Niche: <20% of respondents indicated that their organizations have adopted these technologies.
Using 10+ years Using 5-10 years Using 2-5 years Using <2 years Plan to in 12 months Plan to in 1-3 years Not sure No plans
Learning Management Systems (LMS)
SCORM-based eLearning
Virtual Classroom / Webinar Tools
Performance Support / Job Aids
Video-Based Learning Platforms
Mobile Learning / Apps
Screen Reader / Accessibility Solutions
Learning Analytics / Data Visualization Platforms
Community of Practice / Discussion Board Solutions
AI-Based Learning Personalization
AR (Augmented Reality) for Learning
VR (Virtual Reality) for Learning
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Not surprisingly, L&D professionals rely
most on established tools like LMSs,
SCORM, and virtual classrooms
All of the survey respondents were presented with this question about
their learning technology adoption. Not surprisingly, the most widely
adopted technologies in our sample were:
• Learning Management Systems (LMS) (90.8%)
• SCORM-based eLearning (81.7%)
• Virtual Classroom / Webinar Tools (84.7%)
These tools form the core infrastructure of many L&D programs,
reecting widespread deployment and long-standing reliance within
the organizational learning ecosystems. Their consistent presence over
time suggests that, while innovation continues across the learning
tech landscape, these foundational tools remain irreplaceable for many
practitioners.
In contrast, emerging tools such as AI-based learning personalization,
VR/AR, and chatbots are adopted less commonly and for shorter
periods of time.
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Niche
Selective
Common
Mainstream
Adoption Rates for Core Set of Learning Technologies
Grouped by adoption rate, then sorted by % of users with 10+ years experience.
Adoption Rate
Using 10+ years Using 5-10 years Using 2-5 years Using <2 years Plan to in 12 months Plan to in 1-3 years Not sure No plans
%
90.8
81.7
84.7
75.7
60.2
52.8
52.8
48.0
47.6
47.0
35.5
32.7
31.5
26.7
24.9
23.1
19.9
15.1
17.1
16.1
Learning Management Systems (LMS)
SCORM-based eLearning
Virtual Classroom / Webinar Tools
Performance Support / Job Aids
Video-Based Learning Platforms
Mobile Learning / Apps
Generative AI for Content Development
Screen Reader / Accessibility Solutions
Learning Analytics / Data Visualization Platforms
Community of Practice / Discussion Board Solutions
Digital Badges / Micro-Credentialing
Learning Content Management Systems (LCMS)
Automated Translation / Localization Tools
Learning Experience Platforms (LXP)
Gamication Engines
xAPI for learning (Tin Can API)
AI-Based Learning Personalization
AR (Augmented Reality) for Learning
VR (Virtual Reality) for Learning
Chatbots for Learning
N = 502
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Adoption Rates for Core Set of Learning Technologies
This table provides the detail behind the graph on the prior page.
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Adoption Rates for Core Set of Learning Technologies
This table provides the detail behind the graph on the prior page.
L&D professionals favor supportive tools
like surveys, screen recordings, and polling
tools,whereas many newer technologies
remain on the sidelines
A subset of respondents (152) chose to continue answering the
adoption question, covering 23 additional learning technologies,
and we’ve applied the same adoption groupings to this subset.
The most widely adopted tools in this set include:
• Survey / Feedback Platforms (83.6%)
• Screencasting / Screen Recording Tools (84.2%)
• Live Polling / Quizzing Tools (80.3%)
While these tools may not function as core learning platforms,
they also exhibit widespread, long-term use.
A second tier of tools—such as mentoring platforms, skills
mapping/competency management, and adaptive learning
systems—shows a mix of planned adoption, limited current use,
and notable uncertainty.
At the lower end of adoption, several tools powered by AI,
automation, or immersive technology—such as biometric
engagement detection, immersive gaming simulations,
and Robotic Process Automation (RPA)—remain largely
underutilized.
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Niche
Selective
Common
Mainstream
Adoption Rates for Additional Set of Learning Technologies
Grouped by adoption rate, then sorted by % of users with 10+ years experience.
Adoption Rate %
83.6
84.2
80.3
59.2
38.8
37.5
27.6
23.7
33.6
34.9
28.9
27.0
20.4
24.3
20.4
21.7
19.7
14.5
12.5
7.9
7.2
17.1
6.6
Survey / Feedback Platforms
Screencasting / Screen Recording Tools
Live Polling / Quizzing Tools
Microlearning Solutions
Virtual Labs / Software Simulation Platforms
360-Degree Feedback / Assessment Systems
Blended Learning Orchestration Tools
Telepresence Solutions
Mentoring / Coaching Platforms
Skills Mapping / Competency Management Tools
On-the-Job Performance Tracking/Workow Learning Systems
Proctoring / Remote Exam Solutions
E-Portfolio Tools
Immersive Gaming Simulations
Learning Record Store (LRS)
Voice-Enabled Learning Solutions
Adaptive Learning Systems
AI Chatbots for Personalized Learning
xAPI for non-eLearning activities
Intelligent Tutoring Systems
Robotic Process Automation (RPA)
Automated Content Curation / Recommendation Engines
Biometric Engagement Detection / Analytics Tools
Using 10+ years Using 5-10 years Using 2-5 years Using <2 years Plan to in 12 months Plan to in 1-3 years Not sure No plans
N = 152
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Adoption Rates for Additional Set of Learning Technologies
This table provides the detail behind the graph on the prior page.
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Adoption Rates for Additional Set of Learning Technologies
This table provides the detail behind the graph on the prior page.
Some “tried and true” technologies have stood the test of time
More than 80% of respondents said their organization already has these “tried and true” technologies in place. The few who
haven’t adopted them yet are not planning to do so anytime soon.
In this space, new technology acquisition often means switching providers rather than bringing in something new. That kind
of change brings its own set of complications: data migration, systems integration, and the change management required
to get people on board with the new thing that feels a lot like the old thing.
What’s surprising, though, is how these mature technologies don’t show a consistent pattern when it comes to market
consolidation. In some areas, the landscape has clearly narrowed: virtual classrooms and SCORM-based elearning authoring
are dominated by just a handful of major players. In another area, like LMSs, the market remains highly fragmented, with
lots of smaller providers each claiming a slice of the pie.
While these tools are “tried and true,” the provider ecosystem is anything but settled.
Survey / Feedback Platforms
Screencasting / Screen Recording Tools
Live Polling / Quizzing Tools
Learning Management Systems (LMS)
SCORM-based eLearning
90.8
81.7
84.7
83.6
84.2
80.3
Virtual Classroom / Webinar Tools
N = 152
N = 502
Adoption Rate %
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Some “late bloomers” and newcomers are gaining traction with
recent adoption
We found a second group of technologies where at least 20% of organizations have adopted them in just the last ve
years. That kind of growth signals momentum—whether it's a new innovation or a long-standing solution nally getting its
moment.
No surprise here: generative AI for content development and automated translation tools are gaining ground fast. These
tools are new enough that many organizations are still experimenting—but their promise of speed and scale makes them
hard to ignore.
What is surprising is who else shows up in this fast-adoption crowd. Mobile learning platforms, digital badging, live polling
tools, and screencasting software—none of these are new, yet they’re experiencing what may feel like a late surge in
popularity.
And then there are screen readers and accessibility solutions. These tools aren’t new. Nor are the people who rely on them.
Nor are the laws in the U.S., Canada, or Europe that require them. But in the last few years, accessibility has earned a louder
voice in L&D—and that attention is pushing these technologies to the forefront, where they should’ve been all along.
Generative AI for Content Development
Screen Reader / Accessibility Solutions
Learning Analytics /
Data Visualization Platforms
Mobile Learning / Apps
Digital Badges / Micro-Credentialing
Microlearning Solutions
Mentoring / Coaching Platforms
52.8
48.0
31.5
47.6
59.2
33.6
52.8
35.5
Automated Translation /
Localization Tools
N = 152
N = 502
Adoption Rate %
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Both AI and not-necessarily-AI tools are rising priorities
A third group of technologies caught our attention: those poised for near-term growth. In this category, 20% or more of L&D
professionals said their organizations plan to adopt these tools in the next three years.
Unsurprisingly, this list is dominated by AI—either technologies that are pure AI tools or ones that become exponentially
more scalable because of it. This includes technologies like AI-powered learning personalization, chatbots, automated
content curation, and adaptive learning systems.
But it’s not all about AI. Mobile learning platforms, digital badging, microlearning solutions and mentoring platforms aren't
new, yet they're experiencing what may feel like a late surge in popularity. While these aren’t inherently AI-based, almost all
of them can (and may already) benet from the scale and power of AI.
AI-Based Learning Personalization
Chatbots for Learning
Generative AI for Content Development
Learning Analytics /
Data Visualization Platforms
Digital Badges / Micro-Credentialing
AI Chatbots for
Personalized Learning
On-the-Job Performance Tracking /
Workow Learning Systems
Adaptive Learning Systems
Automated Content Curation /
Recommendation Engines
Skills Mapping / Competency
Management Tools
19.9
16.1
52.8
47.6
14.5
34.9
17.1
28.9
19.7
35.5
N = 152
N = 502
Adoption Rate %
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AI is everywhere
(even where it’s not)
At rst, we considered putting all the AI-centric technologies on
a single page for comparative analysis, just to see what insights
might emerge. But as we started sorting through the list—asking
“Is this driven by AI?”—it became clear: that’s the wrong question.
At this point, nearly every technology on our list is driven by AI in
some capacity. Some tools have had machine learning baked in
for years. Others are newer to the space, adding on generative AI
features as fast as they can. Take eLearning authoring platforms,
for instance: traditionally not AI-powered, but now many are
rolling out AI-driven content generation, image selection, or even
voiceovers.
MOTIVATIONS & BARRIERS TO ADOPTION
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Engagement, performance, and personalization
are key motivations for technology adoption cited
by both L&D professionals and their technology
providers
We asked both L&D professionals and providers
to select the top four motivations driving their
organization’s adoption of new or advanced learning
technologies. The results show broad alignment on core
goals, without signicant difference between the two
groups.
Overall motivations for adopting new technologies
focused on the value to the learner:
• 72% to increase learner engagement or interactivity
• 67% to improve employee performance or
productivity
• 45% to personalize or tailor learning experiences
Operational motivations to support L&D’s needs were
less prominent motivations for acquiring new learning
technologies:
• 39% to enhance measurement and analytics
capabilities
• 32% to reduce long-term training costs
• 28% to collect better data about the learning
experience
Motivations that focus on perceptions or executive
requests tended to be rated lower among the
motivations cited:
• 38% sought to keep pace with industry trends or
competitive pressures
• 20% sought to address leadership or stakeholder
mandates
This chart compares L&D professionals and providers, but
the sample sizes differ. Fewer providers responded, so small
differences should not be overinterpreted.
L&D Professionals, n = 502
Providers, n = 54
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Budget, time, and leadership buy-in are shared
barriers, but L&D professionals highlight security,
while providers stress ROI
We asked both L&D professionals and providers to
select the top four biggest barriers to adopting new
or advanced learning technologies. The results reveal
some alignment and some divergence in what each
group experiences within their own organizations.
What unites both groups:
Limited budget or funding, lack of time or competing
priorities, and lack of leadership or stakeholder buy-in all
appear in the top four for both groups. These consistent
responses reect the universal challenges of resourcing,
prioritization, and internal advocacy in tech adoption.
Where perspectives diverge:
• L&D professionals were more likely to highlight
security or privacy concerns, presumably reecting
their responsibility for safeguarding employee data
and aligning with IT policies.
• Providers, on the other hand, emphasized unclear
ROI or measurable impact, possibly reecting their
challenge in proving value to clients before making
tech investments.
This chart compares L&D professionals and providers, but
the sample sizes differ. Fewer providers responded, so small
differences should not be overinterpreted.
L&D Professionals, n = 502
Providers, n = 54
RECOMMENDATIONS
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Recommendations for L&D Professionals
If you are building your org’s learning tech ecosystem maturity...
• For organizations taking a conservative or incremental approach: Start with the foundational tools
that have stood the test of time. Learning Management Systems (LMSs), virtual classroom tools, and
SCORM-based elearning remain widely used and well-supported across the industry. Their long history
of adoption means there’s a wealth of implementation knowledge, vendor stability, and user familiarity
to draw from. If you’re looking for a low-risk way to scale up delivery, these tools provide a solid,
predictable backbone.
• For organizations looking to leapfrog ahead: You don’t have to retrace the same steps others took a
decade ago. Instead, consider tools that reect where the industry is headed—like workow learning
systems, AI-powered personalization, or microlearning platforms. While these may require more
experimentation and change management, they can help you design a more exible, data-driven
ecosystem from the start.
If you are seeking solutions within an already mature ecosystem... Look toward technologies with
accelerating adoption in the last ve years. Tools like generative AI for content, accessibility solutions, and
automated translation are gaining traction and can expand your reach or enhance the learner experience.
Their growing presence across organizations suggests rising viability and interest.
Evaluate the pace and scale of real-world adoption—don’t rely solely on industry buzz. Our adoption
data reveals that not all widely discussed technologies have seen widespread use. While some tools attract
attention at conferences and in media, actual implementation often lags behind the hype. Use adoption
timelines and peer benchmarks to inform your decisions, focusing on technologies that not only show
promise but are gaining traction across organizations like yours.
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Recommendations for Learning
Technology Providers
The data shows that two of the commonly followed business strategies—competing in crowded (Red
Ocean) spaces and creating new demand in less-contested (Blue Ocean) areas—are viable and active
within today’s learning technology landscape.
If you're working in a Red Ocean with mature markets and high adoption: Technologies like LMSs,
virtual classrooms, and SCORM-based elearning are already widely adopted across the industry. In these
established markets, competition is high and differentiation becomes more challenging. The focus here is
often on standing out through differences in product features, implementation experience, or measurable
outcomes.
If you’re building in a Blue Ocean with less-adopted or emerging technologies: Our data shows low
current adoption but high future interest in tools like AI-powered personalization, adaptive learning, and
skills mapping. This is your Blue Ocean—markets with less entrenched competition but clear indicators of
growth.
Here's the bottom line for vendors and providers.
AI shows up across the spectrum of technologies in this report—from mature tools that are layering in new
AI features to emerging platforms that are built on AI from the ground up.
Whether you’re operating in a crowded Red Ocean or exploring a less-contested Blue Ocean, AI can be a
powerful engine for differentiation. In established markets, it may enhance existing capabilities like search,
content creation, or analytics. In emerging markets, AI often forms the core value proposition, enabling
new levels of personalization, automation, or scale.
Regardless of market strategy, the question isn’t just “Does your product include AI?” but rather “How does
AI improve the learner’s experience or the organization’s outcomes?”
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Improving the Research: Plans for Future
Research
We learned a lot—not just from the data itself, but from the process of collecting it. Here’s what we’d do
differently next time—and why it matters.
Manage survey fatigue differently.
This time, we split a long list of 43 technologies into two sets in an attempt to reduce cognitive load
on respondents. About 500 answered the core 20, and 150 of them opted in for the additional set of 23
technologies. In future iterations, we’d randomize subsets and distribute them evenly across participants.
We’d also consider using incentives to increase completion rates and balance sample sizes.
Clarify ambiguous response categories.
Our current “Not sure” and “Other” categories introduced unnecessary ambiguity. Does “Not sure” mean
the respondent knows the technology is used but not when, or that they don’t know if it’s used at all?
Future surveys could separate these out to allow for clearer interpretation.
Fully embrace the implications of a convenience sample.
This study used a convenience sample—primarily Learning Guild members who responded to email or
to the Guild’s and Megan’s social media invites. While directionally valid for making relatively low-stakes
internal insights, it’s important to note that results may not generalize to the entire L&D industry.
Include qualitative inputs from respondents.
It was beyond the scope of this study to gather stories and examples about why organizations are
making the learning technology choices they’re making. A future study could investigate this.
About the authors
Megan Torrance is the CEO and founder of
TorranceLearning, where she leads a team of
learning experience designers & engineers in
reimagining workplace learning. Her rm has
served Fortune 1000 companies, major research
universities, global professional associations,
and federal agencies for over two decades.
Megan brings a strategic vision that helps
TorranceLearning bridge vision with execution—
delivering innovative, data-informed, and
learner-centered solutions.
Megan is the author of Agile for Instructional
Designers, Data & Analytics for Instructional
Designers, Making Sense of xAPI and the
upcoming book The AI Implementation Guide
for L&D. She facilitates in several of eCornell’s
certicate programs focused on power
dynamics, communication skills and women’s
executive leadership.
Megan Torrance
CEO of TorranceLearning
Lauren Milstid M.S.
Senior Learning Experience
Designer
Lauren Milstid, M.S., has 15 years of experience
in learning and development and is the
Senior Learning Experience Designer at
TorranceLearning. She thrives at the intersection
of L&D, leadership, and Lean Six Sigma, using
these strengths to drive performance and
streamline processes. Lauren specializes in
distilling complex ideas into clear, actionable
solutions that help learners, stakeholders,
and teams focus on what matters most. She
facilitates Learning Project Ignition sessions to
align learning with business goals and translates
data into informed decisions and impactful
learning experiences. Lauren co-created the AI
Driver’s Permit learning product and coaches
teammates on using AI to enhance learning
design and workow efciency.
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About the Learning Guild
Founded in 2002, the Learning Guild is the oldest
and most trusted source of information, networking,
and community for eLearning professionals. As a
member-driven organization, the Learning Guild is
built upon a core belief: together we are better. The Guild community is made
up of a network of learning professionals who work in corporate, government,
and academic settings, all exploring solutions to their unique challenges.
Our members enjoy hundreds of eLearning resources every year that help
them stay up to date on best practices and new techniques in L&D. We also
offer paid content packages that enhance free membership. Membership
includes benets such as access to online events, discounts on our face-to-face
conferences, research, eBooks, white papers, Learning Solutions, and more.
Learn more about Guild membership and content packages.
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the Guild.
The Guild grants a limited, non-exclusive, non-transferable license
to each active member to use the Materials in accordance with the
following terms and conditions:
1. Except as otherwise restricted in this License Agreement,
Licensee may read, download, and print the Materials for
Licensee’s personal use for purposes of research, evaluation,
development, and testing in order to advance knowledge in the
eld of eLearning.
2. Licensee may cite, reproduce, or copy up to four statistics,
tables, graphs, or charts in any 12-month period, but may not
reproduce images that show product comparisons without
written permission from the Guild. Additional citations,
reproductions, or copies may be made only with written
permission from the Guild.
3. The Guild must be cited as the source of any original statistics,
tables, graphs, charts, or any other Materials copied or
reproduced by Licensee. The citation to the Guild as the
source must be in eight point font or larger, and be placed
immediately following the portion of the Materials used by
Licensee.
4. Licensee may not use or distribute the materials for commercial
purposes, directly or indirectly. Commercial use or distribution
of the Materials is permitted only pursuant to a separate reprint/
redistribution commercial license agreement between Licensee
and the Guild. The Guild retains all commercial rights in the
Materials.
5. This License Agreement grants to Licensee no right, title, or interest
in or to the Guild’s copyrights or other intellectual property in the
Materials. Other than the specic rights granted by this License
Agreement, the Guild retains all right, title, and interest in and to
the Materials.
6. The Guild makes no representations or warranties of any kind,
express or implied, with regard to the Materials. The Guild makes
no express or implied warranties of merchantability or tness for
a particular purpose with regard to the Materials, and no warranty
that the use of the Materials will not infringe any patent, copyright,
trademark, or other intellectual or proprietary rights.
7. Licensee agrees to use the materials in compliance with all
applicable laws.
8. In any use of the Materials by Licensee, Licensee may not, in any
way, indicate or imply that the Guild has endorsed Licensee or its
products.
9. Neither the Guild, nor its employees, agents, or representatives,
will be liable or responsible to Licensee in any manner whatsoever
for damages of any nature, incidental, consequential, or punitive,
arising from the termination of this License Agreement or the use
of the Materials by Licensee.
10. The provisions of the Privacy, Membership, and Terms of
Use Agreement between Licensee and the Guild, including
specically but without limitation the Guild Research section of
such agreement, are incorporated in this License Agreement by
reference, and are a part of this License Agreement.
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