by post_author

DRIVR is a new learning platform that aims to teach people working in quickly dwindling industries (such as coal mining) about emerging fields that may be the future of their employment. It is targeted towards people who are fearful that their job might be ending in a week, month, or year.

The platform is designed to tailor lessons to the current knowledge of the students who sign up to use the service. DRIVR begins with an introductory survey that asks students question about their technical skills and other preferences, such as, “Are you willing to move for a new job?” This process aims to personalize the platform for students so it makes them feel more connected to DRIVR.

DRIVR will then automatically provide a list of options for interactive courses about emerging industries from driverless cars to coding. The courses will be created in collaboration with companies who are industry leaders—providing a prime opportunity for integration with job opportunities for the student.

DRIVR encourages user engagement by making the program gamified and goal-oriented. First, all of the lessons are organized as a back-and-forth between a student and a “tutor”, or an artificial intelligence bot that interacts with the student (similar to the Economist GMAT tutor software). The program presents learning material—in the form of text, photos, charts, and videos—as the student proceeds through the program. The program constantly asks the student to answer a variety of multiple choice questions, sentence completion questions, and review questions. The program is computer adaptive, so if the student answers incorrectly, they receive an explanation for why it is wrong, and if they are correct, they can quickly move on. At the end of each session, the student will be updated on their progress with a message like, “Congratulations! You have completed 5% of Automated Cars. Come back tomorrow to build your skills for a better future.”

Designed in this way, the program effectively motivates students by giving them hope that they will be qualified for jobs in emerging fields if they get the pink slip for their current profession.

Further, the lessons force students to interact with the material (via the back-and-forth with the AI tutor) and provide the necessary triggers to ensure they continue learning about new career fields. Thus, the educational program itself motivates the students—they will feel a small sense of gratification when they get a question correct, and a desire to improve if they get a question wrong. This design ensure that students will keep coming back to DRIVR to boost their skills.

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