This position paper describes how the nascent area of AI for development can learn from the challenges and successes of its parents: artificial intelligence and information and communication technologies for development (ICT4D). AI suffered from overly ambitious beginnings and years of stumbling before finding its footing, and achieving impactful ICT4D has been an equally challenging endeavor. We describe the history and challenges of both AI and ICT4D research, and present three broad suggestions for AI-for-development researchers: (1) that they spend as much time as possible with the kind of site or the organization they are hoping to impact; (2) that they be ambitious but humble in their goals and expectations; and (3) that they put AI in the service of existing, well-intented, competent development organizations.

This case study article describes the iterative design process of an adaptive, mixed-initiative calendaring tool with embedded artificial intelligence. We establish the specific types of assistance in which the target user population expressed interest, and we highlight our findings regarding the scheduling practices and the reminding preferences of these users. These findings motivated the redesign and enhancement of our intelligent system. Lessons learned from the study--namely, highlighting the merits of usability toward widespread adoption and retention, and that simple problems that perhaps do not necessitate complex AI-based solutions should not go unattended merely due to their inherent simplicity--conclude the article, along with a discussion of the importance of the iterative design process for any user adaptive system.

This case study article describes the iterative design process of an adaptive, mixed-initiative calendaring tool with embedded artificial intelligence.  We establish the specific types of assistance in which the target user population expressed interest, and we highlight our findings regarding the scheduling practices and the reminding preferences of these users.  These findings motivated the redesign and enhancement of our intelligent system.  Lessons learned from the study—namely, highlighting the merits of usability toward widespread adoption and retention, and that simple problems that perhaps do not necessitate complex AI-based solutions should not go unattended merely due to their inherent simplicity—conclude the article, along with a discussion of the importance of the iterative design process for any user adaptive system.