Obviously this is not an exhaustive list, however, all of the mentioned approaches did result in benefits for projects I was involved in. I will very briefly touch on each of these approaches and than add a link to more theoretical information:
Challenge-Based Learning: an approach that uses challenges put forward by ngo's, policy makers or industry that need to be solved for societal goals (society at large). What makes this approach match the speed of innovation and emerging new content, is that it offers a way to look at a challenge, break it down into feasable or actionable steps, and gradually move towards solutions or realizations of where new challenges pop-up. The great benefit of this approach is that teachers and experts can be guides on the side, offering their expertise in solving challenges or solving part of the challenge. A great resource for this is the website from the Challenge Based Learning Institute. Multi-actor teamwork (so people working in different sectors coming together) is key for this approach, as solutions can be found in different disciplines.
Case studies. These case studies has been at the center of Harvard business school and has been proven its usefulness over the years. They have a student guide on this as well. The benefit of using a case study approach, is that it also allows you to make informed decisions, and create real-life evaluations for the problems you are addressing. The guide also includes online cases and discussion guidelines. Teamwork is key in this approach, and again teachers are guides on the side.
Mentoring. Any sectorial innovation, brings along the challenge of having to create new content or new processes describing these new innovations (e.g. when we moved from face-to-face to online learning, the best of us looked at other online learning experts to find best practices). Mentors can provide insights into new content, new evolutions, new skills. While innovation-rich sectors move forward rapidly (e.g. space exploration, renewable energy), it becomes difficult to know what is happening and how it feels or looks. By using experts as mentors, the old and proven approach of mentorship gets a new push forward. In each sector you have these experts and some of them are also great educators. By attracting the right experts, you can lift learners to the level of innovation rapidly (mentoring as one-on-one or small group learning).
Data cases. With Big Data embedded in many different sectors, data saviness is becoming critical. Data saviness can have many faces: coding and manipulating data, looking and analysing data through visualisations to name but two. Once data savviness is integrated into learning, more people can integrate data analysis in their own sector and come up with new ideas (e.g. Waze happened as tracking traffic came together with map alternatives, data cases using life energy data streams are used to optimize energy consumption). Embedding or exploring data is a way to enrich learning within certain domains.
Enlarge AI-based EdTech within approaches. The first 3 approaches listed above have a strong emphases on the human creative factor (= coming up with solutions). There is some social learning involved, learners are working collaboratively and across disciplines and sectors. But of course we are now in the AI age, which means automated results and matching based on big data analysis can now be integrated in learning in general. For instance using informal and formal EdTech tools that facilitate learning. A really nice informal bit of EdTech that I am following is https://startlearning.today/ where micro-learning is offered based on your own job profile. Another interesting project is the roll out of adaptive learning like the adaptive learning degree in biology at Arizona state.
(Picture source: https://www.flickr.com/photos/gforsythe/ have a look, she is absolutely great!)