Recent advances in automatic machine learning (aML) allow solving problems without any human intervention. However, sometimes a human-in-the-loop can be beneficial in solving computationally hard problems. In this paper we provide new experimental insights on how we can improve computational intelligence by complementing it with human intelligence in an interactive machine learning approach (iML). For this purpose, we used the Ant Colony Optimization (ACO) framework, because this fosters multi-agent approaches with human agents in the loop. We propose unification between the human intelligence and interaction skills and the computational power of an artificial system. The ACO framework is used on a case study solving the Traveling Salesman Problem, because of its many practical implications, e.g. in the medical domain. We used ACO due to the fact that it is one of the best algorithms used in many applied intelligence problems. For the evaluation we used gamification, i.e. we implemented a snake-like game called Traveling Snakesman with the MAX–MIN Ant System (MMAS) in the background. We extended the MMAS–Algorithm in a way, that the human can directly interact and influence the ants. This is done by “traveling” with the snake across the graph. Each time the human travels over an ant, the current pheromone value of the edge is multiplied by 5. This manipulation has an impact on the ant’s behavior (the probability that this edge is taken by the ant increases). The results show that the humans performing one tour through the graphs have a significant impact on the shortest path found by the MMAS. Consequently, our experiment demonstrates that in our case human intelligence can positively influence machine intelligence. To the best of our knowledge this is the first study of this kind.

This research investigates the effectiveness of gamification in enhancing student engagement, motivation, and learning outcomes in physics education, with a particular emphasis on structural performance modeling through Knowledge Space Theory (KST). The study utilizes Basketball Physics Challenge - a game designed to teach fundamental physics concepts like initial velocity, motion, and trajectory - and compares the outcomes of gamified versus non-gamified learning environments. The experimental design involved 10th grade students from a secondary school in Liechtenstein, using a crossover methodology where participants alternated between gamified and non-gamified versions of the game. The results indicate that gamification significantly boosts engagement and performance metrics, as evidenced by increased time on task, a higher frequency of attempts, and better scores compared to the non-gamified condition. Additionally, gamification positively influences motivation, particularly when experienced before non-gamified sessions. Although quiz performance did not show significant differences between conditions, a strong positive correlation was observed between game scores and quiz points in the gamified condition during the second session. The study, furthermore, analyzed structural performance and learning trajectories, demonstrating that gamification fosters a more dynamic and effective learning process. This study highlights the potential of gamification to transform physics education by enhancing student motivation and engagement, thereby promoting a deeper understanding and retention of complex scientific concepts.

The success of serious games usually depends on their capabilities to engage learners and to provide them with personalized gaming and learning experiences. Therefore, it is important to equip a game, as an autonomous computer system, with a certain level of understanding about individual learning trajectories and gaming processes. AI and machine learning technologies increasingly enter the field; these technologies often fail, however, since serious games either pose highly complex problems (combining gaming and learning process) or do not provide the extensive data bases that would be required. An interesting new direction is augmenting the strength of AI technologies with human intuition and human cognition. In the present paper, we investigated performance of the MAXMIN Ant System, a combinatorial optimization algorithm, with and without human interventions to the algorithmic procedure. As a testbed, we used a clone of the Travelling Salesman problem, the Travelling Snakesman game. We found some evidence that human interventions result in superior performance than the algorithm alone. The results are discussed regarding the applicability of this pathfinding algorithm in adaptive games, exemplified by Micro Learning Space adaptation systems.