artificial intelligence | TechChange | The Institute for Technology and Social Change

What are the most exciting examples of AI for Education Training and Learning, particularly for adult learning and global development?

Here are the 6 things I’m most excited about (some which we’re already doing on the AI front at TechChange)

1. Powering captioning and translation services: Translating training and learning materials has been a huge challenge for TechChange over the past decade. I see huge potential in the global development sector for AI technology to reduce cost and time needed to translate and caption content for our cohort based learning experiences. Live captioning for our events has also been something we’ve worked hard on through partnerships and the AI for live captioning continues to improve. And lastly our multilingual learning and events platform is already available in dozens of languages but AI tech has opened up the possibility for hundreds more less spoken languages to more efficiently be used in our stack.

2. Creating more opportunities for accessibility. The AI accessibility revolution is upon us. We are big on inclusion at TechChange from making our courses 508 compliant to providing sign language service options for our partners for virtual and hybrid conferences. AI tech will allow us to provide more inclusive spaces for those who need it.

3. Generating virtual environments and scenarios for simulation-based learning: Simulations are a powerful way to build skills and demonstrate learning. I see huge potential for AI to power the building of complex scenarios and environments quickly and efficiently, especially when paired with VR/AR. At TechChange we have designed many 3D virtual environments for events and conferences and are excited to be piloting some immersive training projects with several partners this year. Get in touch if you are interested in working with us on this.

4. Recommendation and matching engines: At TechChange we believe that building relationships is as important as building skills. We are social creatures and we learn better with other people. That’s why we’re excited to experiment in the coming months with engines and tools that help facilitate networking connections and personalized learning experiences for our learning cohorts.

5. Summary generation: ChatGPT and other tools are great for generating content but I am really excited about summary generation. We have been capturing summaries for meetings, summaries for trainings, summaries for conferences, etc. – Both text based and video based – that can be easily processed and shared at speed. In today’s FOMO world I think this is a powerful application that will only improve.

6. AI-supported workshops and trainings: We also see potential for using ChatGPT and other tools within flow of activities exercises to support learning outcomes for both in person and virtual experiences. AI can help to do time bound do-read outs of group conversations, support with research and synthesis prompts, pair groups and participants up more effectively based on specific criteria and more.

7. ???

Educators and global development professionals- Curious what you think? How are you using AI to support your learning outcomes?

Some other news…

We are excited to bring back our AI for global development online cohort course. This was one of our more popular offerings from 2017-2020 and given the interest and demand in 2023, we are thrilled to be revamping it and adding additional content related to ethics and responsible data.

We don’t have a specific date yet but you can register for the waitlist here.

Get in touch if you want to explore how TechChange can support you in delivering best-in-class AI-powered learning and convening experiences.

When I hear the term “artificial intelligence”, my first thoughts go to HAL9000 and Data from Star Trek before settling on some vague notion about the Turing test. Clearly I’m not a computer scientist.

While reading Patrick Meier’s book, I realized that I had missed out on a wide range of advances in the field of machine learning, some of which also fall under “artificial intelligence”, which can help us make sense of the onslaught of information that we are faced with whenever a disaster strikes.

When the crowd gets overwhelmed

While nothing can beat the collective intelligence of a sufficiently large group of people that focus their energy on processing a lot of data, the problem with this type of crowdsourcing is that you need a very large group of people – and volunteers are a scarce resource.

Projects like Artificial Intelligence for Disaster Response (AIDR) from the Qatar Computing Research Institute (QCRI) are striving to make better use of the volunteer’s time. To do this, the AIDR algorithm is basically looking over the volunteers’ shoulders while they are processing a small amount of data. The machine learns from every decision, until it understands the patterns well enough to process the data itself. Datasets it is unsure about are returned to the volunteers for review and their decisions then improve the algorithm further. According to QCRI, the algorithm frequently reaches confidence levels of over 80 per cent, meaning that huge amounts of data can be analysed in a fraction of the time it would take volunteers.

You can test AIDR for free one the project’s website. If you want to know more, take a look at the video below.

Image analysis

The EU’s Joint Research Centre (JRC) as well DigitalGlobe, a company that provides satellite imagery and analysis, go even further: they are training their algorithms to interpret images. The JRC algorithm for example is already able to detect rubble for damage assessments in a city after an earthquake with up to 92% confidence, while DigitalGlobe is asking the crowd to teach its software how to recognize buildings on satellite photos. That information will no doubt be used to improve the company’s commercial products, but it is also being used to help fight malaria in Swaziland by providing aid organizations with a better idea of population density. This in turn can help program managers make decisions about where to commit the most resources.

You can support the malaria project through DigitalGlobe’s Tomnod platform here.

Another example, where this type of automatic population density data would have been useful, is the Ebola response, where population data had to be estimated manually, based on houses that first had to be mapped by OpenStreetMap volunteers. An algorithm that can automatically identify homes would have been much faster.

What I find amazing is that these tools are already working and available today. And while there are definitely still ways to improve them and bugs to work out, they make me very optimistic for the very near future of information management and needs assessment in disaster response.

What do you think about the role of artificial intelligence in disaster response? Comment below or tweet at us @TechChange. This post originally appeared on Social Media 4 Good.

If you are interested in learning about technologies like artificial intelligence that are helping in disaster response, join us in our upcoming online course “Technology for Disaster Response” which starts on 22 June.

About author

Timo Luege, TC103: Technology for Disaster Response Facilitator

After nearly ten years of working as a journalist (online, print and radio), Timo worked four years as a Senior Communications Officer for the International Federation of Red Cross and Red Crescent Societies (IFRC) in Geneva and Haiti. During this time, he also launched the IFRC’s social media activities and wrote the IFRC social media staff guidelines. He then worked as Protection Delegate for International Committee of the Red Cross (ICRC) in Liberia before starting to work as a consultant. His clients include UN agencies and NGOs. Among other things, he wrote the UNICEF “Social Media in Emergency Guidelines” and contributed to UNOCHA’s “Humanitarianism in the Network Age”. Over the last year, Timo advised UNHCR- and IFRC-led Shelter Clusters in Myanmar, Mali and most recently the Philippines on Communication and Advocacy. He blogs at Social Media for Good.