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The multidisciplinary nature of learning engineering creates challenges. The problems that learning engineering attempts to solve often require expertise in diverse fields such as [[software engineering]], [[instructional design]], [[___domain knowledge]], [[pedagogy]]/[[andragogy]], [[psychometrics]], [[learning sciences]], [[data science]], and [[systems engineering]]. In some cases, an individual Learning Engineer with expertise in multiple disciplines might be sufficient. However, learning engineering problems often exceed any one person’s ability to solve.
A 2021 convening of thirty learning engineers produced recommendations that key challenges and opportunities for the future of the field involve enhancing R&D infrastructure, supporting ___domain-based education research, developing components for reuse across learning systems, enhancing human-computer systems, better engineering implementation in schools, improving advising, optimizing for the long-term instead of short-term, supporting 21st-century skills, improved support for learner engagement, and designing algorithms for equity.
== See also ==
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