Deep reinforcement learning using convolutional neural networks is the technology behind autonomous vehicles. Could this same technology facilitate the road to college? During the summer between high school and college, college-related tasks that students must navigate can hinder successful matriculation. We employ conversational artificial intelligence (AI) to efficiently support thousands of would-be college freshmen by providing personalized, text message–based outreach and guidance for each task where they needed support. We implemented and tested this system through a field experiment with Georgia State University (GSU). GSU-committed students assigned to treatment exhibited greater success with pre-enrollment requirements and were 3.3 percentage points more likely to enroll on time. Enrollment impacts are comparable to those in prior interventions but with substantially reduced burden on university staff. Given the capacity for AI to learn over time, this intervention has promise for scaling personalized college transition guidance.

How an Artificially Intelligent Virtual Assistant Helps Students Navigate the Road to College

Simulation of human intelligence process is made possible with the help of artificial intelligence. The learning, reasoning and self-correction properties are made possible in computer systems. Along with AI, other technologies are combined effectively in order to create remarkable applications. We apply the changing role of AI and its techniques in new educational paradigms to create a personalised teaching-learning environment. Features like recognition, pattern matching, decision making, reasoning, problem solving and so on are applied along with knowledge based system and supervised machine learning for a complete learning and assessment process.

https://www.irojournals.com/aicn/V1/I1/01.pdf

Implementation of artificial intelligence in imparting education and evaluating student performance

The advancement of technology, especially the development and application of artificial intelligence, has deeply affected the education sector and brought opportunities for pedagogical adaptation. Intelligent tutoring systems, a major application of artificial intelligence in education, have drawn extensive concerns. However, in reality, the penetration rate of intelligent tutoring systems and the enthusiasm of faculty to use are still relatively low. This research examined the determinants of the willingness of faculty to use intelligent tutoring systems. Innovation diffusion theory was the theoretical basis of this research and it was adapted by incorporating perceived trust and experience. To gather data, a cross-sectional questionnaire survey was performed and structural equation modeling was employed to analyze the data. The findings indicated that relative advantage, compatibility, perceived trust and experience are the contributing determinants of the willingness of faculty to use intelligent tutoring systems, while complexity has no significant effect. Meanwhile, complexity is significantly negatively affected by experience and compatibility. Relative advantage is significantly positively affected by perceived trust but not by complexity. Based on the research findings, relevant recommendations for encouraging faculty to use intelligent tutoring systems were proposed.

Participant or Spectator? Comprehending the Willingness of Faculty to Use Intelligent Tutoring Systems in the Artificial Intelligence Era.

Artificial intelligence (AI) is the fourth industrial revolution, and in higher education it will fundamentally transform the work of academics and higher education administrators. AI will also have several implications for students, including a reconsideration of what sorts of skills and knowledge should be taught and developed in the academy to prepare students for digital working lives. As social work educators, we have begun to wonder what the implications of AI in the academy will have for social work education. For educators, there are implications for pedagogy that go well beyond the introduction of new digital tools that merely integrate into existing teaching models and practices. This is because AI will not simply add to existing teaching modes and practices, but will fundamentally transform teaching and learning. Drawing on recent literature and research into in AI and higher education, this article explores possible future implications for social work education. We identify the transformative and disruptive potential of AI in higher education, and consider how this intersects with the ethical and relational side of social work as a profession. Implications for social work education are canvassed

Problematising Artificial Intelligence in Social Work Education: Challenges, Issues and Possibilities

Usos y efectos de la inteligencia artificial en educación

Mandy Barrett, Southwest Virginia Community College, mandy.barrett@sw.edu Lisa Branson, Northern Virginia Community College, lbranson@nvcc.edu Sheryl Carter, New River Community College, scarter@nr.edu Frank DeLeon, Northern Virginia Community College, fdeleon@nvcc.edu Justin Ellis, Reynolds Community College, jellis@reynolds.edu Cirrus Gundlach, Paul D. Camp Community College, cgundlach@pdc.edu Dale Lee, Mountain Empire Community College, dlee@mecc.edu

/pdf/using-artificial-intelligence-to-enhance-educational-4mzs3ihje2.pdf

Cirrus Gundlach

Papers

Using Artificial Intelligence to Enhance Educational Opportunities and Student Services in Higher Education.